MIT spinout New Valence Robotics (NVBOTS) has brought to market the only fully automated commercial 3-D printer that’s equipped with cloud-based queuing and automatic part removal, making print jobs quicker and easier for multiple users, and dropping the cost per part.

If you haven’t used a 3-D printer yet, you may be surprised to learn that it isn’t fully automated the way your office’s inkjet is.

With paper printers, users queue documents from a computer, and each finished sheet drops neatly into a tray, waiting to be collected. With commercial 3-D printers, however, designs are manually programmed into the printer, and each finished part is manually removed before starting a new print, which is very time-consuming. At schools and businesses, a trained expert usually handles all prints, which can be expensive.

Now MIT spinout New Valence Robotics (NVBOTS) has brought to market the only fully automated commercial 3-D printer that’s equipped with cloud-based queuing and automatic part removal, making print jobs quicker and easier for multiple users, and dropping the cost per part.

To use the printer, called NVPro, a user submits a project from any device, which queues up in the NVCloud software. When a part gets printed, a retractable blade cuts the piece out and moves it into a bin, and the next project begins automatically. Projects can be monitored remotely via webcam.

Invented several years ago in an MIT fraternity house’s basement and commercially launched last April, the printer is now used at more than 100 businesses and schools, primarily as an education tool. Over the past year, there have been more than 84,000 prints, saving more than 165,000 labor hours, according to the startup.

NVLabs, the startup’s research arm, is now bolstering security, analyzing big data, working with materials, and improving human-robotic interfaces for 3-D printing. In January, NVLabs spun out Digital Alloys, a startup developing high-speed, multimetal manufacturing systems that run at lower costs than traditional systems.

The company envisions a world in which 3-D printing is as easy and commonplace as printing on paper — and perhaps more globally accessible, says chair and co-founder Alfonso (A.J.) Perez ’13, MEng ’14. “Our mission is to allow printing with any material, at any time, and from anywhere in the world. This means you could be on a beach in Bora Bora and control a machine at MIT with the click of one button,” he says.

The other co-founders are Chris Haid ’14, general manager at NVBOTS; Forrest Pieper ’14, director of software at Digital Alloys; and Mateo Peña Doll, director of hardware engineering at Digital Alloys.

From the basement to NVBOTS

As undergraduates, the four NVBOTS co-founders were brothers of Phi Delta Theta and dedicated “makers.” Outside the classroom, they worked on various projects that involved 3-D printing parts, such as medical devices and a number of other gadgets. But whenever they wanted a part, they’d submit a computer-aided design (CAD) file to a 3-D printer location on campus and wait for the product, which took days.

To expedite production, the group built a custom 3-D printer in the fraternity house’s basement. It was exciting at first, but everyone in the house soon wanted to print at the same time. The solution was a cloud-based queue and webcam to monitor the printer which, at the time, was the first of its kind, Perez says.

But each part still needed to be manually removed. “If it was 3:00 in the morning, and you wanted to start the next job, you had to get up and go down into the basement and start it,” Perez says. “That was an untenable solution for us lazy students.”

To rectify that issue, the students created an automated part-ejection mechanism. After a part has been printed, the print plate is lowered. A blade moves over to the plate, wedging underneath the part. As it moves across the plate, the blade cuts away the part and pushes it into a bin. The blade retracts and the plate rises back up.

Being new to the technology, Perez was shocked that, despite decades of operation, 3-D printers couldn’t automatically eject parts. The four students filed patents with the MIT Technology Licensing Office but didn’t plan on becoming entrepreneurs. That changed after they brought to the 2013 MIT Tech Fair “a quintessential MIT prototype — a rat’s nest of wires,” Perez says.

Numerous 3-D-printing experts asked the students where to buy the automatic part-remover. Moreover, novices didn’t realize the technology and the queuing software were new. “When the early adopters — the experts — are saying that this is the solution to a problem, and the general population already expects the solution to exist, that was the ‘ah-ha’ moment,” Perez says.

That summer, “things got serious,” Perez says, when the co-founders entered the Global Founders Skills Accelerator (now MIT delta v), held in the Martin Trust Center for MIT Entrepreneurship. There, they fleshed out a business plan and learned about profit margins, earning customers, and company ownership, and they officially incorporated NVBOTS.

For 6-year-olds and PhDs

NVBOTS initially designed printers for education. The idea is to develop curricula that uses 3-D-printed models as teaching aids for any subject — such as printing Roman figures for history lessons, Egyptian art, or a human heart for an anatomy class.

“If you want to teach the physics of projectile motion, you can use our catapult lesson that teaches about the math by flinging balls across the room,” Perez says. That type of hands-on learning, he adds, helps “tickle those emotions that MIT shoots for — ‘mens et manus,’ mind and hand.”

Pilot programs began in 2014 at Newton North High School and the Sizer School in Fitchburg; the printers are now in dozens of schools in the region and beyond. Printers are usually put in a central area for all to use. Every student can sign up for an account to submit parts for printing, but only one administrator approves or rejects the parts. MIT Copytech is currently piloting a printing service with the NVPro.

The machines require minimal training and oversight, which is a benefit for schools, Perez says. “Teachers like to teach. Teachers don’t like to maintain a machine, and schools can’t afford to pay for someone to run a machine,” he says. “On a basic level, we wanted to create a 3-D printer that’s so easy a 6-year-old could use it, but a PhD could use it as well.”

In fact, Perez notes, the printer only has one button: “the giant emergency stop button.”

Over the past year, the startup has also sold to companies in automotive, construction, medical devices, consumer goods, aerospace, and architecture fields. Usually it takes days or weeks to 3-D print, say, a building model or a medical device part. With NVBOTS, clients can upload a design on the cloud and pick up the product themselves within a day, with no need of a trained expert. The reduced cost of ownership and the labor saved significantly lowers the price per part compared to traditional 3-D printers, Perez says.

This also gives more workers the ability to find solutions for engineering and other company issues, Perez says: “Everyone who signs up for an account can become a problem solver.”

Making metals, improving printers

Moving forward, NVLabs and Digital Alloys will bring even more innovations to 3-D printing, Perez says.

Digital Alloys is developing a new print head that, according to the startup, allows for multimetal prints at significantly higher speeds than traditional methods, for aerospace, defense, and automotive applications.

Traditionally, metal printing systems are a layer-by-layer slog. One layer of powder, about 20 to 50 microns thick, gets spread across a plate. A laser positioned above the plate melts one layer of powder to the layer below. This repeats until the object is complete. Digital Alloys’ process uses thicker wire as the material, which builds up more quickly. The industry leader for metal printing produces 2 kilograms per day, while Digital Alloys produces 20 kilograms, Perez says.

NVLabs is gathering data on NVBOTS printers to improve performance. Each time a user prints, the startup gathers data on the job’s success, quality, length, and material, among other information. Since launch, the startup has generated 3.8 terabytes of data related to the manufacturing process. The lab is also tackling security issues, improving human-machine interfaces, and, as a major challenge, enabling 3-D printing with any plastic.

“There’s a crazy world of polymers that can be used for printing,” Perez says. “That’s a big aspect of what we’re working on.”

MIT Professor Daniela Rus, director of CSAIL, said the goal of a new SystemsThatLearn@CSAIL initiative is “to create a new generation of AI tools that are deeply rooted in systems.” Photo: Jason Dorfman/MIT CSAIL

From self-driving cars to the internet of things, artificial intelligence (AI) has reached new levels of sophistication in recent years. With that in mind, this week MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) launched an industry collaboration focused on using machine learning to create functional human-like systems.

Nearly 40 senior researchers will participate in the new “SystemsThatLearn@CSAIL” (STL) initiative alongside a range of organizations that include founding members BT, Microsoft, Nokia Bell Labs, Salesforce, and Schlumberger. Member companies will work with CSAIL scientists to suggest new lines of research and develop real-world applications.

“Developing capabilities in AI and machine learning are key to the future of fields like finance, energy, manufacturing, and health care,” says STL Executive Director Lori Glover. “While the demand for expertise is great, the supply of talent remains small and unevenly distributed. By democratizing the field of AI, SystemsThatLearn@CSAIL is an effort to address that skills gap.”

STL builds on CSAIL’s Big Data Initiative, which developed tools for handling complex datasets. While many machine-learning solutions are trained and deployed in separate phases, STL aims to integrate these processes, focusing on a range of resources to handle distributed data and computing power.

Another goal is to make key aspects of data science less laborious. A 2016 report found that data scientists spend 80 percent of their time collecting and organizing data, and only 20 percent analyzing it.

“By promoting industry interaction with academia, we’re hoping to create new tools and systems that can increase productivity by automating much of the tedious work of data science,” says MIT Professor Samuel Madden, one of STL’s two faculty leads alongside Professor Tommi Jaakkola. “We are already seeing that areas like autonomous vehicles and personalized health care have the potential to transform entire industries.”

In her opening remarks, CSAIL Director Daniela Rus described AI and systems researchers as two communities that would benefit from stronger collaboration.

“Our grand vision is to create a new generation of AI tools that are deeply rooted in systems and that can make those systems better,” Rus said. “My aspiration is to get to a place where machine learning becomes a normal part of what an operating system does.”

One STL project is the data discovery tool “Data Civilizer,” which allows organizations to discover related datasets from thousands of distinct business databases and files. Another is “Model DB,” a machine-learning management system that saves time for data scientists and lets them easily correlate performance on particular training examples with specific model features.

According to STL Technical Director Stephen Buckley, many of the software tools they develop will be released under MIT’s open source license.

“The ultimate aim is to democratize access and use of machine learning tools, without requiring advanced knowledge of the underlying technologies,” says Buckley.

Researchers have designed and built a device that responds to mechanical stresses by changing the color of a spot on its surface. Image: Subramanian Sundaram

In this age of smartphones and tablet computers, touch-sensitive surfaces are everywhere. They’re also brittle, as people with cracked phone screens everywhere can attest.

Covering a robot — or an airplane or a bridge — with sensors will require a technology that is both flexible and cost-effective to manufacture in bulk. A team of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory thinks that 3-D printing could be the answer.

In an attempt to demonstrate the feasibility of flexible, printable electronics that combine sensors and processing circuitry and can act on their environments, the researchers have designed and built a device that responds to mechanical stresses by changing the color of a spot on its surface.

The components that make up the printable device. Image: Subramanian Sundaram

The device was inspired by the golden tortoise beetle, or “goldbug,” an insect whose exterior usually appears golden but turns reddish orange if the insect is poked or prodded — that is, mechanically stressed.

“In nature, networks of sensors and interconnects are called sensorimotor pathways,” says Subramanian Sundaram, an MIT graduate student in electrical engineering and computer science (EECS), who led the project. “We were trying to see whether we could replicate sensorimotor pathways inside a 3-D-printed object. So we considered the simplest organism we could find.”

The researchers present their new design in the latest issue of the journal Advanced Materials Technologies. Sundaram is the first author on the paper, and the senior authors are Sundaram’s advisor, Wojciech Matusik, an associate professor of EECS; and Marc Baldo, a professor of EECS and director of the Research Laboratory of Electronics. Joining them on the paper are Pitchaya Sitthi-Amorn, a former postdoc in Matusik’s lab; Ziwen Jiang, an undergraduate EECS student; and David Kim, a technical assistant in Matusik’s Computational Fabrication Group.

Bottom up

Printable electronics, in which flexible circuitry is deposited on some type of plastic substrate, has been a major area of research for decades. But Sundaram says that the ability to print the substrate itself greatly increases the range of devices the technique can yield.

For one thing, the choice of substrate limits the types of materials that can be deposited on top of it. Because a printed substrate could consist of many materials, interlocked in intricate but regular patterns, it broadens the range of functional materials that printable electronics can use.

Printed substrates also open the possibility of devices that, although printed as flat sheets, can fold themselves up into more complex, three-dimensional shapes. Printable robots that spontaneously self-assemble when heated, for instance, are a  topic of ongoing research at the CSAIL Distributed Robotics Laboratory, led by Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT.

“We believe that only if you’re able to print the underlying substrate can you begin to think about printing a more complex shape,” Sundaram says.

Selective signaling

The MIT researchers’ new device is approximately T-shaped, but with a wide, squat base and an elongated crossbar. The crossbar is made from an elastic plastic, with a strip of silver running its length; in the researchers’ experiments, electrodes were connected to the crossbar’s ends. The base of the T is made from a more rigid plastic. It includes two printed transistors and what the researchers call a “pixel,” a circle of semiconducting polymer whose color changes when the crossbars stretch, modifying the electrical resistance of the silver strip.

In fact, the transistors and the pixel are made from the same material; the transistors also change color slightly when the crossbars stretch. The effect is more dramatic in the pixel, however, because the transistors amplify the electrical signal from the crossbar. Demonstrating working transistors was essential, Sundaram says, because large, dense sensor arrays require some capacity for onboard signal processing.

“You wouldn’t want to connect all the sensors to your main computer, because then you would have tons of data coming in,” he says. “You want to be able to make clever connections and to select just the relevant signals.”

To build the device, the researchers used the MultiFab, a custom 3-D printer developed by Matusik’s group. The MultiFab already included two different “print heads,” one for emitting hot materials and one for cool, and an array of ultraviolet light-emitting diodes. Using ultraviolet radiation to “cure” fluids deposited by the print heads produces the device’s substrate.

Sundaram added a copper-and-ceramic heater, which was necessary to deposit the semiconducting plastic: The plastic is suspended in a fluid that’s sprayed onto the device surface, and the heater evaporates the fluid, leaving behind a layer of plastic only 200 nanometers thick.

Fluid boundaries

A transistor consists of semiconductor channel on top of which sits a “gate,” a metal wire that, when charged, generates an electric field that switches the semiconductor between its electrically conductive and nonconductive states. In a standard transistor, there’s an insulator between the gate and the semiconductor, to prevent the gate current from leaking into the semiconductor channel.

The transistors in the MIT researchers’ device instead separate the gate and the semiconductor with a layer of water containing a potassium salt. Charging the gate drives potassium ions into the semiconductor, changing its conductivity.

The layer of saltwater lowers the device’s operational voltage, so that it can be powered with an ordinary 1.5-volt battery. But it does render the device less durable. “I think we can probably get it to work stably for two months, maybe,” Sundaram says. “One option is to replace that liquid with something between a solid and a liquid, like a hydrogel, perhaps. But that’s something we would work on later. This is an initial demonstration.”

“I am very impressed with both the concept and the realization of the system,” says Hagen Klauk, who leads the Organic Electronic Research Group at the Max Planck Institute for Solid State Research, in Stuttgart, Germany. “The approach of printing an entire optoelectronic system — including the substrate and all the components — by depositing all the materials, including solids and liquids, by 3-D printing is certainly novel, interesting, and useful, and the demonstration of the functional system confirms that the approach is also doable. By fabricating the substrate on the fly, the approach is particularly useful for improvised manufacturing environments where dedicated substrate materials may not be available.”

Engineers have designed a microfluidic device they call a “tree-on-a-chip,” which mimics the pumping mechanism of trees and other plants.

Trees and other plants, from towering redwoods to diminutive daisies, are nature’s hydraulic pumps. They are constantly pulling water up from their roots to the topmost leaves, and pumping sugars produced by their leaves back down to the roots. This constant stream of nutrients is shuttled through a system of tissues called xylem and phloem, which are packed together in woody, parallel conduits.

Now engineers at MIT and their collaborators have designed a microfluidic device they call a “tree-on-a-chip,” which mimics the pumping mechanism of trees and plants. Like its natural counterparts, the chip operates passively, requiring no moving parts or external pumps. It is able to pump water and sugars through the chip at a steady flow rate for several days. The results are published this week in Nature Plants.

Anette “Peko” Hosoi, professor and associate department head for operations in MIT’s Department of Mechanical Engineering, says the chip’s passive pumping may be leveraged as a simple hydraulic actuator for small robots. Engineers have found it difficult and expensive to make tiny, movable parts and pumps to power complex movements in small robots. The team’s new pumping mechanism may enable robots whose motions are propelled by inexpensive, sugar-powered pumps.

“The goal of this work is cheap complexity, like one sees in nature,” Hosoi says. “It’s easy to add another leaf or xylem channel in a tree. In small robotics, everything is hard, from manufacturing, to integration, to actuation. If we could make the building blocks that enable cheap complexity, that would be super exciting. I think these [microfluidic pumps] are a step in that direction.”

Hosoi’s co-authors on the paper are lead author Jean Comtet, a former graduate student in MIT’s Department of Mechanical Engineering; Kaare Jensen of the Technical University of Denmark; and Robert Turgeon and Abraham Stroock, both of Cornell University.

A hydraulic lift

The group’s tree-inspired work grew out of a project on hydraulic robots powered by pumping fluids. Hosoi was interested in designing hydraulic robots at the small scale, that could perform actions similar to much bigger robots like Boston Dynamic’s Big Dog, a four-legged, Saint Bernard-sized robot that runs and jumps over rough terrain, powered by hydraulic actuators.

“For small systems, it’s often expensive to manufacture tiny moving pieces,” Hosoi says. “So we thought, ‘What if we could make a small-scale hydraulic system that could generate large pressures, with no moving parts?’ And then we asked, ‘Does anything do this in nature?’ It turns out that trees do.”

The general understanding among biologists has been that water, propelled by surface tension, travels up a tree’s channels of xylem, then diffuses through a semipermeable membrane and down into channels of phloem that contain sugar and other nutrients.

The more sugar there is in the phloem, the more water flows from xylem to phloem to balance out the sugar-to-water gradient, in a passive process known as osmosis. The resulting water flow flushes nutrients down to the roots. Trees and plants are thought to maintain this pumping process as more water is drawn up from their roots.

“This simple model of xylem and phloem has been well-known for decades,” Hosoi says. “From a qualitative point of view, this makes sense. But when you actually run the numbers, you realize this simple model does not allow for steady flow.”

In fact, engineers have previously attempted to design tree-inspired microfluidic pumps, fabricating parts that mimic xylem and phloem. But they found that these designs quickly stopped pumping within minutes.

It was Hosoi’s student Comtet who identified a third essential part to a tree’s pumping system: its leaves, which produce sugars through photosynthesis. Comtet’s model includes this additional source of sugars that diffuse from the leaves into a plant’s phloem, increasing the sugar-to-water gradient, which in turn maintains a constant osmotic pressure, circulating water and nutrients continuously throughout a tree.

Running on sugar

With Comtet’s hypothesis in mind, Hosoi and her team designed their tree-on-a-chip, a microfluidic pump that mimics a tree’s xylem, phloem, and most importantly, its sugar-producing leaves.

To make the chip, the researchers sandwiched together two plastic slides, through which they drilled small channels to represent xylem and phloem. They filled the xylem channel with water, and the phloem channel with water and sugar, then separated the two slides with a semipermeable material to mimic the membrane between xylem and phloem. They placed another membrane over the slide containing the phloem channel, and set a sugar cube on top to represent the additional source of sugar diffusing from a tree’s leaves into the phloem. They hooked the chip up to a tube, which fed water from a tank into the chip.

With this simple setup, the chip was able to passively pump water from the tank through the chip and out into a beaker, at a constant flow rate for several days, as opposed to previous designs that only pumped for several minutes.

“As soon as we put this sugar source in, we had it running for days at a steady state,” Hosoi says. “That’s exactly what we need. We want a device we can actually put in a robot.”

Hosoi envisions that the tree-on-a-chip pump may be built into a small robot to produce hydraulically powered motions, without requiring active pumps or parts.

“If you design your robot in a smart way, you could absolutely stick a sugar cube on it and let it go,” Hosoi says.

This research was supported, in part, by the Defense Advance Research Projects Agency.

Researchers including MIT professor Daniela Rus (left) and research scientist Stephanie Gil (right) have developed a technique for preventing malicious hackers from commandeering robot teams’ communication networks. To verify the theoretical predictions, the researchers implemented their system using a battery of distributed Wi-Fi transmitters and an autonomous helicopter. Image: M. Scott Brauer.

Distributed planning, communication, and control algorithms for autonomous robots make up a major area of research in computer science. But in the literature on multirobot systems, security has gotten relatively short shrift.

In the latest issue of the journal Autonomous Robots, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and their colleagues present a new technique for preventing malicious hackers from commandeering robot teams’ communication networks. The technique could provide an added layer of security in systems that encrypt communications, or an alternative in circumstances in which encryption is impractical.

“The robotics community has focused on making multirobot systems autonomous and increasingly more capable by developing the science of autonomy. In some sense we have not done enough about systems-level issues like cybersecurity and privacy,” says Daniela Rus, an Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT and senior author on the new paper.

“But when we deploy multirobot systems in real applications, we expose them to all the issues that current computer systems are exposed to,” she adds. “If you take over a computer system, you can make it release private data — and you can do a lot of other bad things. A cybersecurity attack on a robot has all the perils of attacks on computer systems, plus the robot could be controlled to take potentially damaging action in the physical world. So in some sense there is even more urgency that we think about this problem.”

Identity theft

Most planning algorithms in multirobot systems rely on some kind of voting procedure to determine a course of action. Each robot makes a recommendation based on its own limited, local observations, and the recommendations are aggregated to yield a final decision.

A natural way for a hacker to infiltrate a multirobot system would be to impersonate a large number of robots on the network and cast enough spurious votes to tip the collective decision, a technique called “spoofing.” The researchers’ new system analyzes the distinctive ways in which robots’ wireless transmissions interact with the environment, to assign each of them its own radio “fingerprint.” If the system identifies multiple votes as coming from the same transmitter, it can discount them as probably fraudulent.

“There are two ways to think of it,” says Stephanie Gil, a research scientist in Rus’ Distributed Robotics Lab and a co-author on the new paper. “In some cases cryptography is too difficult to implement in a decentralized form. Perhaps you just don’t have that central key authority that you can secure, and you have agents continually entering or exiting the network, so that a key-passing scheme becomes much more challenging to implement. In that case, we can still provide protection.

“And in case you can implement a cryptographic scheme, then if one of the agents with the key gets compromised, we can still provide  protection by mitigating and even quantifying the maximum amount of damage that can be done by the adversary.”

Hold your ground

In their paper, the researchers consider a problem known as “coverage,” in which robots position themselves to distribute some service across a geographic area — communication links, monitoring, or the like. In this case, each robot’s “vote” is simply its report of its position, which the other robots use to determine their own.

The paper includes a theoretical analysis that compares the results of a common coverage algorithm under normal circumstances and the results produced when the new system is actively thwarting a spoofing attack. Even when 75 percent of the robots in the system have been infiltrated by such an attack, the robots’ positions are within 3 centimeters of what they should be. To verify the theoretical predictions, the researchers also implemented their system using a battery of distributed Wi-Fi transmitters and an autonomous helicopter.

“This generalizes naturally to other types of algorithms beyond coverage,” Rus says.

The new system grew out of an earlier project involving Rus, Gil, Dina Katabi — who is the other Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT — and Swarun Kumar, who earned master’s and doctoral degrees at MIT before moving to Carnegie Mellon University. That project sought to use Wi-Fi signals to determine transmitters’ locations and to repair ad hoc communication networks. On the new paper, the same quartet of researchers is joined by MIT Lincoln Laboratory’s Mark Mazumder.

Typically, radio-based location determination requires an array of receiving antennas. A radio signal traveling through the air reaches each of the antennas at a slightly different time, a difference that shows up in the phase of the received signals, or the alignment of the crests and troughs of their electromagnetic waves. From this phase information, it’s possible to determine the direction from which the signal arrived.

Space vs. time

A bank of antennas, however, is too bulky for an autonomous helicopter to ferry around. The MIT researchers found a way to make accurate location measurements using only two antennas, spaced about 8 inches apart. Those antennas must move through space in order to simulate measurements from multiple antennas. That’s a requirement that autonomous robots meet easily. In the experiments reported in the new paper, for instance, the autonomous helicopter hovered in place and rotated around its axis in order to make its measurements.

When a Wi-Fi transmitter broadcasts a signal, some of it travels in a direct path toward the receiver, but much of it bounces off of obstacles in the environment, arriving at the receiver from different directions. For location determination, that’s a problem, but for radio fingerprinting, it’s an advantage: The different energies of signals arriving from different directions give each transmitter a distinctive profile.

There’s still some room for error in the receiver’s measurements, however, so the researchers’ new system doesn’t completely ignore probably fraudulent transmissions. Instead, it discounts them in proportion to its certainty that they have the same source. The new paper’s theoretical analysis shows that, for a range of reasonable assumptions about measurement ambiguities, the system will thwart spoofing attacks without unduly punishing valid transmissions that happen to have similar fingerprints.

“The work has important implications, as many systems of this type are on the horizon — networked autonomous driving cars, Amazon delivery drones, et cetera,” says David Hsu, a professor of computer science at the National University of Singapore. “Security would be a major issue for such systems, even more so than today’s networked computers. This solution is creative and departs completely from traditional defense mechanisms.”

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See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

The feedback system enables human operators to correct the robot’s choice in real-time – Jason Dorfman, MIT CSAIL

For robots to do what we want, they need to understand us. Too often, this means having to meet them halfway: teaching them the intricacies of human language, for example, or giving them explicit commands for very specific tasks. But what if we could develop robots that were a more natural extension of us and that could actually do whatever we are thinking?

A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Boston University is working on this problem, creating a feedback system that lets people correct robot mistakes instantly with nothing more than their brains.

Using data from an electroencephalography (EEG) monitor that records brain activity, the system can detect if a person notices an error as a robot performs an object-sorting task. The team’s novel machine-learning algorithms enable the system to classify brain waves in the space of 10 to 30 milliseconds.

While the system currently handles relatively simple binary-choice activities, the paper’s senior author says that the work suggests that we could one day control robots in much more intuitive ways.

“Imagine being able to instantaneously tell a robot to do a certain action, without needing to type a command, push a button or even say a word,” says CSAIL director Daniela Rus. “A streamlined approach like that would improve our abilities to supervise factory robots, driverless cars and other technologies we haven’t even invented yet.”

In the current study the team used a humanoid robot named “Baxter” from Rethink Robotics, the company led by former CSAIL director and iRobot co-founder Rodney Brooks.

The paper presenting the work was written by BU PhD candidate Andres F. Salazar-Gomez, CSAIL PhD candidate Joseph DelPreto, and CSAIL research scientist Stephanie Gil under the supervision of Rus and BU professor Frank H. Guenther.  The paper was recently accepted to the IEEE International Conference on Robotics and Automation (ICRA) taking place in Singapore this May.

Past work in EEG-controlled robotics has required training humans to “think” in a prescribed way that computers can recognize. For example, an operator might have to look at one of two bright light displays, each of which corresponds to a different task for the robot to execute.

The downside to this method is that the training process and the act of modulating one’s thoughts can be taxing, particularly for people who supervise tasks in navigation or construction that require intense concentration.

Rus’ team wanted to make the experience more natural. To do that, they focused on brain signals called “error-related potentials” (ErrPs), which are generated whenever our brains notice a mistake. As the robot indicates which choice it plans to make, the system uses ErrPs to determine if the human agrees with the decision.

The team uses EEG brain signals to detect if the person notices a mistake – Jason Dorfman, MIT CSAIL

“As you watch the robot, all you have to do is mentally agree or disagree with what it is doing,” says Rus. “You don’t have to train yourself to think in a certain way –  the machine adapts to you, and not the other way around.”

ErrP signals are extremely faint, which means that the system has to be fine-tuned enough to both classify the signal and incorporate it into the feedback loop for the human operator.

In addition to monitoring the initial ErrPs, the team also sought to detect “secondary errors” that occur when the system doesn’t notice the human’s original correction.

“If the robot’s not sure about its decision, it can trigger a human response to get a more accurate answer,” says Gil. “These signals can dramatically improve accuracy, creating a continuous dialogue between human and robot in communicating their choices.”

The feedback system enables human operators to correct the robot’s choice in real-time – Jason Dorfman, MIT CSAIL2

While the system cannot yet recognize secondary errors in real time, Gil expects the model to be able to improve to upwards of 90 percent accuracy once it can.

In addition, since ErrP signals have been shown to be proportional to how egregious the robot’s mistake is, the team believes that future systems could extend to more complex multiple-choice tasks.

Salazar-Gomez notes that the system could even be useful for people who can’t communicate verbally: a task like spelling could be accomplished via a series of several discrete binary choices, which he likens to an advanced form of the blinking that allowed stroke victim Jean-Dominique Bauby to write his memoir “The Diving Bell and the Butterfly.”

“This work brings us closer to developing effective tools for brain-controlled robots and prostheses,” says Wolfram Burgard, a professor of computer science at the University of Freiburg who was not involved in the research. “Given how difficult it can be to translate human language into a meaningful signal for robots, work in this area could have a truly profound impact on the future of human-robot collaboration.”

Read the paper here.

The project was funded in part by Boeing and the National Science Foundation.

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• MIT CSAIL’s 6-foot-tall NASA humanoid robot has landed
• Researchers add a splash of human intuition to planning algorithms
• Wearable AI that can detect the tone of a conversation
• Ingestible robots, glasses-free 3-D, and computers that explain themselves
• Design, simulate and build a custom drone

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

A new system called Data Civilizer automatically finds connections among many different data tables and allows users to perform database-style queries across all of them. The results of the queries can then be saved as new, orderly data sets that may draw information from dozens or even thousands of different tables.

The age of big data has seen a host of new techniques for analyzing large data sets. But before any of those techniques can be applied, the target data has to be aggregated, organized, and cleaned up.

That turns out to be a shockingly time-consuming task. In a 2016 survey, 80 data scientists told the company CrowdFlower that, on average, they spent 80 percent of their time collecting and organizing data and only 20 percent analyzing it.

An international team of computer scientists hopes to change that, with a new system called Data Civilizer, which automatically finds connections among many different data tables and allows users to perform database-style queries across all of them. The results of the queries can then be saved as new, orderly data sets that may draw information from dozens or even thousands of different tables.

“Modern organizations have many thousands of data sets spread across files, spreadsheets, databases, data lakes, and other software systems,” says Sam Madden, an MIT professor of electrical engineering and computer science and faculty director of MIT’s bigdata@CSAIL initiative. “Civilizer helps analysts in these organizations quickly find data sets that contain information that is relevant to them and, more importantly, combine related data sets together to create new, unified data sets that consolidate data of interest for some analysis.”

The researchers presented their system last week at the Conference on Innovative Data Systems Research. The lead authors on the paper are Dong Deng and Raul Castro Fernandez, both postdocs at MIT’s Computer Science and Artificial Intelligence Laboratory; Madden is one of the senior authors. They’re joined by six other researchers from Technical University of Berlin, Nanyang Technological University, the University of Waterloo, and the Qatar Computing Research Institute. Although he’s not a co-author, MIT adjunct professor of electrical engineering and computer science Michael Stonebraker, who in 2014 won the Turing Award — the highest honor in computer science — contributed to the work as well.

Pairs and permutations

Data Civilizer assumes that the data it’s consolidating is arranged in tables. As Madden explains, in the database community, there’s a sizable literature on automatically converting data to tabular form, so that wasn’t the focus of the new research. Similarly, while the prototype of the system can extract tabular data from several different types of files, getting it to work with every conceivable spreadsheet or database program was not the researchers’ immediate priority. “That part is engineering,” Madden says.

The system begins by analyzing every column of every table at its disposal. First, it produces a statistical summary of the data in each column. For numerical data, that might include a distribution of the frequency with which different values occur; the range of values; and the “cardinality” of the values, or the number of different values the column contains. For textual data, a summary would include a list of the most frequently occurring words in the column and the number of different words. Data Civilizer also keeps a master index of every word occurring in every table and the tables that contain it.

Then the system compares all of the column summaries against each other, identifying pairs of columns that appear to have commonalities — similar data ranges, similar sets of words, and the like. It assigns every pair of columns a similarity score and, on that basis, produces a map, rather like a network diagram, that traces out the connections between individual columns and between the tables that contain them.

Tracing a path

A user can then compose a query and, on the fly, Data Civilizer will traverse the map to find related data. Suppose, for instance, a pharmaceutical company has hundreds of tables that refer to a drug by its brand name, hundreds that refer to its chemical compound, and a handful that use an in-house ID number. Now suppose that the ID number and the brand name never show up in the same table, but there’s at least one table linking the ID number and the chemical compound, and one linking the chemical compound and the brand name. With Data Civilizer, a query on the brand name will also pull up data from tables that use just the ID number.

Some of the linkages identified by Data Civilizer may turn out to be spurious. But the user can discard data that don’t fit a query while keeping the rest. Once the data have been pruned, the user can save the results as their own data file.

“Data Civilizer is an interesting technology that potentially will help data scientists address an important problem that arises due to the increasing availability of data — identifying which data sets to include in an analysis,” says Iain Wallace, a senior informatics analyst at the drug company Merck. “The larger an organization, the more acute this problem becomes.”

“We are currently exploring how to use Civilizer as a harmonization layer on top of a variety of chemical-biology datasets,” Wallace continues. “These datasets typically link compounds, diseases, and targets together. One use case is to identify which table contains information about a specific compound and what additional information is available about that compound in other related datasets. Civilizer helps us by allowing full text search over all the columns and then identifying related columns automatically. By using Civilizer, we should be easily able to add additional data sources and update our analysis very quickly.”

Read the paper here.

PhD student Tao Du watching the bunnycopter take off . Image credit: Jason Dorfman, MIT CSAIL

This fall’s new FAA regulations have made drone flight easier than ever for both companies and consumers. But what if the drones out on the market aren’t exactly what you want?

A new system from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is the first to allow users to design, simulate and build their own custom drone. Users can change the size, shape and structure of their drone based on the specific needs they have for payload, cost, flight time, battery usage and other factors.
To demonstrate, researchers created a range of unusual-looking drones, including a five-rotor “pentacopter” and a rabbit-shaped “bunnycopter” with rotors of different sizes and heights.

“This system opens up new possibilities for how drones look and function,” says MIT professor Wojciech Matusik, who oversaw the project in CSAIL’s Computational Fabrication Group. “It’s no longer a one-size-fits-all approach for people who want to make and use drones for particular purposes.”

The interface lets users design drones with different rotors and rods. It also provides guarantees that its drones can take off, hover and land (which is no simple task considering the intricate technical trade-offs associated with drone weight, shape and control).

“For example, adding more rotors generally lets you carry more weight, but you also need to think about how to balance the drone to make sure it doesn’t tip,” says PhD student Tao Du, who is first author on a related paper about the system. “Irregularly-shaped drones are very difficult to stabilize, which means that they require establishing very complex control parameters.”

Du and Matusik co-authored a paper with PhD student Adriana Schulz, postdoctoral researcher Bo Zhu and assistant professor Bernd Bickel of IST Austria. It will be presented this week at the annual SIGGRAPH Asia conference in Macao, China.

How it works
Today’s commercial drones only come in a small range of options, typically with an even number of upward-facing rotors. But there are many emerging use cases for other kinds of drones. For example, having an odd number of rotors might create a clearer view for a drone’s camera, or allow the drone to carry objects with unusual shapes.

Designing these less conventional drones, however, often requires expertise in multiple disciplines, including control systems, fabrication and electronics.

“Developing multicopters like these that are actually flyable involves a lot of trial-and-error, tweaking the balance between all the propellers and rotors,” says Du. “It would be more or less impossible for an amateur user, especially one without any computer-science background.”

But the CSAIL group’s new system makes the process much easier. Users design drones by choosing from a database of parts and specifying their needs for things like payload, cost and battery usage. The system computes the sizes of design elements like rod lengths and motor angles, and looks at metrics such as torque and thrust to determine whether the design will actually work. It also uses an “LQR controller” that takes information about a drone’s characteristics and surroundings to optimize its flight plan.

One of the project’s core challenges stemmed from the fact that a drone’s shape and structure (its “geometry”) is usually strongly tied to how it has been programmed to move (its “control”). To overcome this, researchers used what’s called an “alternating direction method,” which means that they reduced the number of variables by fixing some of them and optimizing the rest. This allowed the team to decouple the variables of geometry and control in a way that optimizes the drone’s performance.

“Once you decouple these variables, you turn a very complicated optimization problem into two easy sub-problems that we already have techniques for solving,” says Du.

Du envisions future versions of the system that could proactively give design suggestions, like recommending where a rotor should go to accommodate a desired payload.

“This is the first system in which users can interactively design a drone that incorporates both geometry and control,” says Nobuyuki Umetani, a research scientist at Autodesk, Inc who was not involved in the paper. “This is very exciting work that has the potential to change the way people design.”

The project was supported in part by the National Science Foundation, the Air Force Research Laboratory and the European Union’s Horizon 2020 research and innovation program.

Click here to read the full paper.

If you enjoyed this article, you might also be interested in:

• Emerging patchwork of regulation of UAS operations near critical infrastructure
• Protecting infrastructure and innovation, under Section 2209 of the FAA extension
• Why DJI is succeeding in making and selling drones and 3DR didn’t
• MIT CSAIL’s 6-foot-tall NASA humanoid robot has landed

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Whitman Richards. Photo: Webb Chappell/MIT Media Lab

Whitman Richards ’53, PhD ’65, professor emeritus of cognitive sciences and of media arts and sciences and principal investigator in the Computer Science and Artificial Intelligence Laboratory, died on Sept. 16 after a long battle with myelofibrosis. One of the first four PhD graduates of the Department of Brain and Cognitive Sciences (BCS), his more than 60 years at MIT were marked by a dedication to the experimental and theoretical study of vision, perception, and cognition.

Richards began his affiliation with MIT as an undergraduate, matriculating in 1950. His decision to return to MIT for graduate work was greatly inspired by a meeting with BCS founder and then department head Professor Hans-Lukas Teuber.

“In the 1960’s, with the advent of accessible computer technology, the development of information theory, and the single electrode, there was renewed excitement about prospects for modeling and understanding mind and brain,” Richards said in a 2004 interview. “Teuber’s charisma and broad vision for a new psychol­ogy was a powerful draw [to the department]. …There was a unique opportunity for a non-traditional grounding in a discipline otherwise mired in tradition.”

Richards’ early research pursued traditional psychophysical experimental methods to study the mechanisms of color perception and stereovision. In the 1970s, his research direction and methodology shifted dramatically after meeting noted physiologist David Marr, who he eventually recruited to MIT. Instead of relying on the traditional experimental methods that had characterized his early career, Richards, Marr, and colleagues began to look for the deep, underlying mathematical principles that allowed a human or artificial visual system to look at the world and make accurate inferences about what the system saw or perceived.

“The breadth of his research was really quite remarkable,” says Josh Tenenbaum, MIT professor of computational cognitive science and former Richards graduate student. “As his career developed, he transitioned from studying the parts of vision that are very close to neural mechanisms, to computational representations of perception, to Bayesian statistical models of perception and cognition. He became almost a computational social scientist — he was incredibly flexible in his thinking.”

Richards’ passionate advocacy for the computational approach to studying visual perception helped to create and nurture the department’s early computational research initiatives.

“Whit’s connection with David Marr back in the late ’70s is really the genesis of modern computational social science today,” says MIT Professor Alex Pentland, the Toshiba Professor of Media Arts and Science and a former Richards graduate student.

Alongside his impressive research legacy, which includes the publication of eight books and over 200 articles, Richards was also regarded by his students and colleagues as a superlative mentor. Many of his former students have found success in a variety of different fields, including psychology, cognitive science, computer science, media, computer graphics, and the defense industry.

“Whitman was an incredibly dedicated advisor. His strategy was to have very few students and make a huge personal investment in each of them,” says John Rubin, a former graduate student of Richards and current executive producer with Tangled Bank Studios at the Howard Hughes Medical Institute. “He was really great at keeping enthusiasm high in his lab, which took all kinds of forms, but included croquet parties at his home, which were terrifically fun. He was always available and, in fact, it was hard for me to keep up with the amount of time he wanted to devote to our joint work! He was indefatigable and devoted.”

Richards is survived by his wife of 54 years, Waltraud Weller Richards, and three daughters: Diana Richards Doyle and husband Mark S. Doyle of Green Cove Springs, Florida; Sylvia Richards-Gerngross and husband Tillman Gerngross of Hanover, New Hampshire; and Eleanor “Nora” Richards Bender and husband Thomas A. Bender of Dedham, Massachusetts. He is also survived by his two siblings: Lincoln K. Richards and wife Gerda of Wellesley, Massachusetts, and Sylvia Richards Messner of Cave Creek, Arizona; and by two grandchildren, Morgan Kelly Doyle and Serafina Richards-Gerngross. Memorial services will be private.

Credit: Jason Dorfman, MIT CSAIL

Anyone who’s watched drone videos or an episode of BattleBots knows that robots can break – and often it’s because they don’t have the proper padding to protect themselves.

But this week researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) will present a new method for 3-D printing soft materials that make robots safer, more resilient, and more precise in their movements – and that could be used to improve the durability of drones, phones, shoes, helmets and more.

The team’s “Programmable Viscoelastic Material” (PVM) technique allows users to program every single part of a 3D-printed object to the exact levels of stiffness and elasticity they want, depending on the task they need for it.

For example, after 3-D printing a cube robot that moves by bouncing, the researchers outfitted it with shock-absorbing “skins” that reduce the amount of energy it transfers to the ground 250 percent.

“That reduction makes all the difference for preventing a rotor from breaking off of a drone or a sensor from cracking when it hits the floor,” says CSAIL director Daniela Rus, who oversaw the project and co-wrote a related paper. “These materials allow us to 3-D print robots with visco-elastic properties that can be inputted by the user at print-time as part of the fabrication process.”

The skins also allow the robot to land nearly four times more precisely, suggesting that similar shock absorbers could be used to help extend the lifespan of delivery drones like the ones being developed by Amazon and Google.

The new paper will be presented at next week’s IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) in Korea. It was written by Rus alongside three postdoctoral associates: lead authors Robert MacCurdy and Jeffrey Lipton, as well as third author Shuguang Li.

Putting a damper on things

There are many reasons for dampers, from controlling the notes of a piano, to keeping car tires on the ground, to protecting structures like radio towers from storms.

The most common damper materials are “viscoelastics” like rubber and plastic that have both solid and liquid qualities. Viscoelastics are cheap, compact and easy to find, but are generally only commercially available in specific sizes and at specific damping levels because of how time-consuming it is to customize them.

The solution, the team realized, was 3-D printing. By being able to deposit materials with different mechanical properties into a design, 3-D printing allows users to “program” material to their exact needs for every single part of an object.

“It’s hard to customize soft objects using existing fabrication methods, since you need to do injection moulding or some other industrial process,” says Lipton. “3-D printing opens up more possibilities and lets us ask the question, ‘can we make things we couldn’t make before?”

Using a standard 3-D printer, the team used a solid, a liquid and a flexible rubber-like material called TangoBlack+ to print both the cube and its skins. The PVM process is related to Rus’ previous 3-D printed robotics work, with an inkjet depositing droplets of different material layer-by-layer and then using UV light to solidify the non-liquids.

The cube robot includes a rigid body, two motors, a microcontroller, battery and IMU sensors. Four layers of looped metal strip serve as the springs that propel the cube.

“By combining multiple materials to achieve properties that are outside the range of the base material, this work pushes the envelope of what’s possible to print,” says Hod Lipson, a professor of engineering at Columbia University and co-author of “Fabricated: The New World of 3-D Printing.” “On top of that, being able to do this in a single print-job raises the bar for additive manufacturing.”

Rus says that PVMs could have many other protective uses, including shock-absorbing running shoes and headgear. By damping the motion brought about by robots’ motors, for example, PVMs are not only able to protect sensitive parts like cameras and sensors, but can also actually make the robots easier to control.

“Being able to program different regions of an object has important implications for things like helmets,” says MacCurdy. “You could have certain parts made of materials that are comfortable for your head to rest on, and other shock-absorbing materials for the sections that are most likely to be impacted in a collision.”

This work was supported by a grant from the National Science Foundation.

Read the research paper here.

From L-R: PhD Fadel Adib, PhD Mingmin Zhao and Professor Dina Katabi demonstrating different ’emotions’ like the picture. Credit: Jason Dorfman, MIT CSAIL

By measuring your heartbeat and breath, this device from MIT’s Computer Science and Artificial Intelligence Lab can tell if you’re excited, happy, angry or sad
.

As many a relationship book can tell you, understanding someone else’s emotions can be a difficult task. Facial expressions aren’t always reliable: a smile can conceal frustration, while a poker face might mask a winning hand.

But what if technology could tell us how someone is really feeling?

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed “EQ-Radio,” a device that can detect a person’s emotions using wireless signals.

By measuring subtle changes in breathing and heart rhythms, EQ-Radio is 87 percent accurate at detecting if a person is excited, happy, angry or sad – and can do so without on-body sensors or facial-recognition software.

MIT professor and project lead Dina Katabi envisions the system being used in entertainment, consumer behavior and health-care. Film studios and ad agencies could test viewers’ reactions in real-time, while smart homes could use information about your mood to adjust the heating or suggest that you get some fresh air.

Professor Dina Katabi (middle) explains how PhD Fadel Adib’s face (right) is neutral, but that EQ-Radio’s analysis of his heartbeat and breathing show that he is sad. Credit: Jason Dorfman MIT CSAIL

“Our work shows that wireless signals can capture information about human behavior that is not always visible to the naked eye,” says Katabi, who co-wrote a paper on the topic with PhD students Mingmin Zhao and Fadel Adib. “We believe that our results could pave the way for future technologies that could help monitor and diagnose conditions like depression and anxiety.”

EQ-Radio builds on Katabi’s continued efforts to use wireless to measure human behavior like breathing and falling. She says that she will incorporate emotion-detection into her spin-off company Emerald, which makes a device that is aimed at detecting and predicting falls among the elderly.

Using wireless signals reflected off people’s bodies, the device measures heartbeats as accurately as an ECG monitor, with a margin of error of approximately 0.3 percent. It then studies the waveforms within each heartbeat to match a person’s behavior to how they previously acted in one of the four emotion-states.

The team will present the work next month at the Association of Computing Machinery’s International Conference on Mobile Computing and Networking (MobiCom).

How it works

Existing emotion-detection methods rely on audiovisual cues or on-body sensors, but there are downsides to both techniques. Cues like facial expressions are famously unreliable, while on-body sensors like chest bands and ECG monitors are inconvenient to wear and become inaccurate if they change position over time.

EQ-Radio instead sends wireless signals that reflect off of a person’s body and back to the device. Its beat-extraction algorithms break the reflections into individual heartbeats and analyze the small variations in heartbeat intervals to determine their levels of arousal and positive affect.

These measurements are what allow EQ-Radio to detect emotion. For example, a person whose signals correlate to low arousal and negative affect is more likely to tagged as sad, while someone whose signals correlate to high arousal and positive affect would likely be tagged as excited.

The exact correlations vary from person to person, but are consistent enough that EQ-Radio could detect emotions with 70 percent accuracy even when it hadn’t previously measured the target person’s heartbeat.

“Just by generally knowing what human heartbeats look like in different emotional states, we can look at a random person’s heartbeat and reliably detect their emotions,” says Zhao.

For the experiments, subjects used videos or music to recall a series of memories that each evoked one the four emotions, as well as a no-emotion baseline. Trained just on those five sets of two-minute videos, EQ-Radio could then accurately classify the person’s behavior among the four emotions 87 percent of the time.

Compared to Microsoft’s vision-based “Emotion API”, which focuses on facial expressions,  EQ-Radio was found to be significantly more accurate in detecting joy, sadness and anger. The two systems performed similarly with neutral emotions, since a face’s absence of emotion is generally easier to detect than its presence.

One of the CSAIL team’s toughest challenges was to tune out irrelevant data. In order to get the heart-rate, for example, the team had to dampen the breathing, since the distance that a person’s chest moves from breathing is much greater than the distance that their heart moves to beat.

To do so, the team focused on wireless signals that are based on acceleration rather than distance traveled, since the rise and fall of the chest with each breath tends to be much more consistent –  and therefore have a lower acceleration – than the motion of the heartbeat.

Although the focus on emotion-detection meant analyzing the time between heartbeats, the team says that the algorithm’s ability to captured the heartbeat’s entire waveform means that in the future it could be used for non-invasive health monitoring and diagnostic settings.

“By recovering measurements of the heart valves actually opening and closing at a millisecond time-scale, this system can literally detect if someone’s heart skips a beat,” says Adib. “This opens up the possibility of learning more about conditions like arrhythmia, and potentially exploring other medical applications that we haven’t even thought of yet.”

Read more about the research here.

By Adam Conner-Simons, MIT CSAIL

This week MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) received an unusual package: a six-foot-tall, 300-pound humanoid robot that NASA hopes to have serve on future space missions to Mars and beyond.

A team of researchers led by CSAIL principal investigator Russ Tedrake will program their new “Valkyrie” robot to autonomously perform a variety of challenging tasks that would allow it to help or even replace astronauts on missions.

Valkyrie is fully electric, with four body cameras, 28 torque-controlled joints and 44 degrees of freedom. The robot boasts more than 200 individual sensors, including 38 on each hand (six on each palm, and eight along each of its four fingers).

Other researchers participating in the project include professors Leslie Kaelbling and Tomas Lozano-Perez, who will conduct work on high-level autonomy.

“Our work is about vetting the robot and seeing what it is capable of,” says Tedrake, whose team received a two-year research grant from NASA for the project. “If we can integrate the autonomy work with our planning and control algorithms, it could result in an unprecedented level of autonomous capabilities for a humanoid robot.’

Tedrake’s team at CSAIL’s Robot Locomotion Group has extensive experience developing autonomous robots. The group spent the last three years doing research as part of the DARPA Robotics Challenge, where they programmed another six-foot-tall robot named Atlas to complete a series of tasks that included opening doors, turning valves, drilling holes, climbing stairs and driving a car.

Besides the CSAIL team, NASA also awarded a Valkyrie robot to Northeastern University in conjunction with the University of Massachusetts at Lowell.

According to CSAIL researchers, a new wireless technology they’ve developed could mean safer drones, smarter homes and password-free WiFi. Source: MIT/CSAIL

By Adam Conner-Simons | CSAIL

We’ve all been there, impatiently twiddling our thumbs while trying to locate a WiFi signal. But what if, instead, the WiFi could locate us?

According to researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), it could mean safer drones, smarter homes and password-free WiFi.

In a new paper, a research team led by professor Dina Katabi presents a system called Chronos that enables a single WiFi access point to locate users to within tens of centimeters, without any external sensors.

The group demonstrated Chronos in an apartment and a cafe, while also showing off a drone that maintains a safe distance from its user with a margin of error of about four centimeters.

“From developing drones that are safer for people to be around, to tracking where family members are in your house, Chronos could open up new avenues for using WiFi in robotics, home automation and more,” says PhD student Deepak Vasisht, who is first author on the paper alongside Katabi and former PhD student Swarun Kumar, who is now an assistant professor at Carnegie Mellon University. “Designing a system that enables one WiFi node to locate another is an important step for wireless technology.”

Experiments conducted in a two-bedroom apartment with four occupants show that Chronos can correctly identify which room a resident is in 94 percent of the time. For the cafe demo, the system was 97 percent accurate in distinguishing in-store customers from out-of-store intruders – meaning it could be used by small businesses to prevent non-customers from stealing their WiFi. (32 percent of Americans have copped to this cyber-crime.)

Chronos locates users by calculating the “time-of-flight” that it takes for data to travel from the user to an access point. The system is 20 times more accurate than existing systems, computing time-of-flight with an average error of 0.47 nanoseconds, or half than one-billionth of a second.

Vasisht presented the paper at this month’s USENIX Symposium on Networked Systems Design and Implementation (NSDI ’16).

How it works

Existing localization methods have required four or five WiFi access points. This is because today’s WiFi devices don’t have wide enough bandwidth to measure time-of-flight, and so researchers have only been able to determine someone’s position by triangulating multiple angles relative to the person.

What Chronos adds is the ability to calculate not just the angle, but the actual distance from a user to an access point, as determined by multiplying the time-of-flight by the speed of light.

“Knowing both the distance and the angle allows you to compute the user’s position using just one access point,” says Deepak Vasisht. “This is encouraging news for the many small businesses and consumers that don’t have the luxury of owning several access points.”

Exploiting the fact that WiFi lets you hop on different frequency channels, the team programmed the system to jump from channel to channel, gathering many different measurements of the distance between access points and the user. Chronos then automatically “stitches” together these measurements to determine the distance.

“By devising a method to rapidly hop across these channels that span almost one gigahertz of bandwidth, Chronos can measure time-of-flight with sub-nanosecond accuracy, emulating with commercial WiFi what has previously needed an expensive ultra-wideband radio,” says Venkat Padmanabhan. a principal researcher at Microsoft Research India. “This is an impressive breakthrough and promises to be a key enabler for applications such as high-accuracy indoor localization.”

That said, getting an accurate time-of-flight with this method still isn’t easy, due to three sets of delays that happen during the transfer.

First, when you wirelessly send a piece of web data, there is a delay in detecting the presence of the “packet” that is hard to distinguish from the actual time-of-flight. To account for it, the team exploits the fact that WiFi uses an encoding method that transmits bits of packets on several even smaller frequencies.

Secondly, if you’re indoors the WiFi signals can bounce off walls and furniture, meaning that the receiver gets several copies of the signal that each experience different times-of-flight. To identify the actual direct path, researchers developed a mechanism to algorithmically determine the delays experienced by all of these copies. From there, they can identify the path with the smallest time-of-flight as the direct path.

Lastly, the team’s channel-hopping approach leads to one other complication: every time Chronos hops to a new band, the hardware resets, adding a delay known as a “phase offset.” To address this the team used the fact that in WiFi, you get an acknowledgement back for each data packet that your phone sends. The team uses these acknowledgements to intelligently cancel out the phase offsets.

The success of Chronos suggests that WiFi-based positioning could help for other situations where there are limited or inaccessible sensors, like finding lost devices or controlling large fleets of drones.

“Imagine having a system like this at home that can continuously adapt the heating and cooling depending on number of people in the home and where they are” says Katabi. “Eliminating the need for cooperation between WiFi routers opens up many exciting new applications for localization.”

Communication system could make it easier to design systems that enable humans and robots to work together in emergency-response teams. Credits image: Jose-Luis Olivares/MIT

By Larry Hardesty, MIT CSAIL

Autonomous robots performing a joint task send each other continual updates: “I’ve passed through a door and am turning 90 degrees right.” “After advancing 2 feet I’ve encountered a wall. I’m turning 90 degrees right.” “After advancing 4 feet I’ve encountered a wall.” And so on.

Computers, of course, have no trouble filing this information away until they need it. But such a barrage of data would drive a human being crazy.

At the annual meeting of the Association for the Advancement of Artificial Intelligence last weekend, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) presented a new way of modeling robot collaboration that reduces the need for communication by 60 percent. They believe that their model could make it easier to design systems that enable humans and robots to work together — in, for example, emergency-response teams.

“We haven’t implemented it yet in human-robot teams,” says Julie Shah, an associate professor of aeronautics and astronautics and one of the paper’s two authors. “But it’s very exciting, because you can imagine: You’ve just reduced the number of communications by 60 percent, and presumably those other communications weren’t really necessary toward the person achieving their part of the task in that team.”

The work could have also have implications for multirobot collaborations that don’t involve humans. Communication consumes some power, which is always a consideration in battery-powered devices, but in some circumstances, the cost of processing new information could be a much more severe resource drain.

In a multiagent system — the computer science term for any collaboration among autonomous agents, electronic or otherwise — each agent must maintain a model of the current state of the world, as well as a model of what each of the other agents takes to be the state of the world. These days, agents are also expected to factor in the probabilities that their models are accurate. On the basis of those probabilities, they have to decide whether or not to modify their behaviors.

Communication costs

In some scenarios, a robot’s decision to broadcast a new item of information could force its fellows to update their models and churn through all those probabilities again. If the information is inessential, broadcasting it could introduce serious delays, to no purpose. And the MIT researchers’ work suggests that 60 percent of communications in multiagent systems may be inessential.

The state-of-the-art method for modeling multiagent systems is called a decentralized partially observable Markov decision process, or Dec-POMDP. A Dec-POMDP factors in several types of uncertainty; not only does it consider whether an agent’s view of the world is correct and whether its estimate of its fellows’ worldviews is correct, it also considers whether any action it takes will be successful. The robot may plan, for instance, to move forward 20 feet but find that crosswinds blow it off course.

Dec-POMDPs generally assume some prior knowledge about the environment in which the agents will be operating. Because Shah and Vaibhav Unhelkar, a graduate student in aeronautics and astronautics and first author on the new paper, were designing a system with emergency-response applications in mind, they couldn’t make that assumption. Emergency-response teams will usually be entering unfamiliar environments, and the very nature of the emergency could render the best prior information obsolete.

Adding the requirement of mapping the environment on the fly, however, makes the problem of computing a multiagent plan prohibitively time consuming. So Shah and Unhelkar’s system ignores uncertainty about actions’ effectiveness and assumes that whatever an agent attempts to do, it will do.

Balancing act

When an agent acquires a new item of information — that, for instance, a given passage through a building is blocked — it has three choices: it can ignore the information; it can use it but not broadcast it; or it can use it and broadcast it.

Each of these choices has benefits but imposes costs. In Shah and Unhelkar’s model, communication is a cost. But if an agent incorporates new information into its own model of the world and doesn’t broadcast it, it also incurs a cost, as its worldview becomes more difficult for its fellows to estimate correctly. For every new item of information an agent acquires, Shah and Unhelkar’s system performs that cost-benefit analysis, based on the agent’s model of the world, its expectations of its fellows’ actions, and the likelihood of accomplishing the joint goal more efficiently.

The researchers tested their system on more than 300 computer simulations of rescue tasks in unfamiliar environments. A version of their system that permitted extensive communication completed the tasks at a rate between 2 and 10 percent higher than the version that reduced communication by 60 percent.

In the experiments, however, all the agents were electronic. “What I’d be willing to bet, although we have to wait until we do the human-subject experiments, is that the human-robot team will fail miserably if the system is just telling the person all sorts of spurious information all the time,” Shah says. “For human-robot teams, I think that this algorithm is going to make the difference between a team that can function effectively versus a team that just plain can’t.”

In a separate research project, members of Shah’s group have asked teams of human subjects to execute similar virtual rescue missions that computer systems did in the experiments reported in the new paper. Using machine-learning algorithms, the researchers have mined the results for statistics on human communication patterns, which can be incorporated into the new model to more explicitly accommodate human-robot teams.

“It is well-understood that in human teams, when one team member gains new information, broadcasting this new information to all team members is generally not a good solution, especially when the cost of communication is high,” says Tim Miller, an assistant professor of computing and information systems at the University of Melbourne in Australia. “This work has applications outside of multiagent systems, reaching into the critical area of human-agent collaboration, where communication can be costly, but more importantly, human team members are quickly overloaded if presented with too much information.”

In a forest simulation, algorithms segment space into “obstacle-free regions” and then link them together to find a single collision-free route.
Photo courtesy of CSAIL researchers.

Adam Conner-Simons | CSAIL

Getting drones to fly around without hitting things is no small task. Obstacle-detection and motion-planning are two of computer science’s trickiest challenges, because of the complexity involved in creating real-time flight plans that avoid obstacles and handle surprises like wind and weather. In a pair of projects announced this week, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) demonstrated software that allow drones to stop on a dime to make hairpin movements over, under, and around some 26 distinct obstacles in a simulated “forest.”

One team’s video shows a small quadrotor doing donuts and figure-eights through an obstacle course of strings and PVC pipes. Weighing just over an ounce and clocking in at 3 and a half inches from rotor to rotor, the drone can fly through the 10-square-foot space at speeds upwards of 1 meter per second.

The team’s algorithms — which are available online and were previously used to plan footsteps for CSAIL’s Atlas robot at last year’s DARPA Robotics Challenge — segment space into “obstacle-free regions” and then link them together to find a single collision-free route.

“Rather than plan paths based on the number of obstacles in the environment, it’s much more manageable to look at the inverse: the segments of space that are ‘free’ for the drone to travel through,” says recent graduate Benoit Landry ’14 MNG ’15, who was first author on a related paper just accepted to the IEEE International Conference on Robotics and Automation (ICRA). “Using free-space segments is a more ‘glass-half-full’ approach that works far better for drones in small, cluttered spaces.”

In a second CSAIL project, PhD student Anirudha Majumdar showed off a fixed-wing plane that is guaranteed to avoid obstacles without any advanced knowledge of the space, and even in the face of wind gusts and other dynamics. His approach was to pre-program a library of dozens of distinct “funnels” that represent the worst-case behavior of the system, calculated via a rigorous verification algorithm.

“As the drone flies, it continuously searches through the library to stitch together a series of paths that are computationally guaranteed to avoid obstacles,” says Majumdar, who was lead author on a related technical report. “Many of the individual funnels will not be collision-free, but with a large-enough library you can be certain that your route will be clear.”

Both papers were co-authored by MIT professor Russ Tedrake; the ICRA paper, which will be presented in May in Sweden, was also co-written by PhD students Robin Deits and Peter R. Florence.

Drones in high-density

A bird might make it seem simple, but flight is a highly complicated endeavor. A flying object can change position in six distinct directions — forward/backward (“surge”), up/down (“heave”), left/right (“sway”), and by rotating front-to-back (“pitch”), side-to-side (“roll”), and horizontally (“yaw”).

“At every moment in time there are 12 distinct numbers needed to describe where the system it is and how quickly it is moving, on top of simultaneously tracking other objects in the space that could get in your way,” says Majumdar. “Most techniques typically can’t handle this sort of complexity in real-time.”

One common motion-planning approach is to sample the whole space through algorithms like the “rapidly-exploring random tree.” Although often effective, sampling-based approaches are generally less efficient and have trouble navigating small gaps between obstacles.

Landry’s team opted to use Deits’ new free-space-based technique, which he calls the “Iterative Regional Inflation by semidefinite programming” algorithm (IRIS). They then coupled IRIS with a “mixed-integer semidefinite program” (MISDP) that assigns specific flight movements to each “space-free region” and then executes the full plan.

To sense its surroundings, the drone used motion-capture optical sensors and an on-board inertial measurement unit (IMU) that help estimate the precise positioning of obstacles.

“I’m most impressed by the team’s ingenious technique of combining on- and off-board sensors to determine the drone’s location,” says Jingjin Yu, an assistant professor of computer science at Rutgers University. “This is key to the system’s ability to create unique routes for each set of obstacles.”

In its current form, MISDP has been optimized such that it can’t do real-time planning; it takes an average of 10 minutes to create a route for the obstacle course. But Landry says that making certain sacrifices would let them generate plans much more quickly.

“For example, you could define ‘free-space regions’ more broadly as links between areas where two or more free-space regions overlap,” says Landry. “That would let you solve for a general motion-plan through those links, and then fill in the details with specific paths inside of the chosen regions. Currently we solve both problems at the same time to lower energy consumption, but if we wanted to run plans faster that would be a good option.”

Majumdar’s software, meanwhile, generates more conservative plans, but can do so in real-time. He first developed a library of 40 to 50 trajectories that are each given an outer bound that the drone is guaranteed to remain within. These bounds can be visualized as ”funnels” that the planning algorithm chooses between to stitch together a sequence of steps that allow the drone to plan its flying on the fly.

A flexible approach like this comes with a high level of guarantees that the software will work, even in the face of uncertainties with both the surroundings and the hardware itself. The algorithm can easily be extended to drones of different sizes and payloads, as well as ground vehicles and walking robots.

As for the environment, imagine the drone choosing between making a forceful roll maneuver that will avoid a tree by a large margin, versus flying straight and avoiding a tree by a small amount.

“A traditional approach might prefer the first since avoiding obstacles by a significant amount seems ‘safer,’” Majumdar says. “But a move like that actually may be riskier because it’s more susceptible to wind gusts. Our method makes these decisions in real-time, which is critical if we want drones to move out of the labs and operate in real-world scenarios.”

A clear path to avoiding obstacles

In a related project, a planner searches a library of pre-computed funnels and selects one that doesn’t intersect with the obstacles.
Photo courtesy of CSAIL researchers.

CSAIL researchers have been working on this problem for many years. Professor Nick Roy has been honing algorithms for drones to develop maps and avoid objects in real-time; in November a team led by PhD student Andrew Barry published a video demonstrating algorithms that allow a drone to dart between trees at speeds of 30 miles per hour.

While these two drones cannot travel quite as fast as Barry’s, their maneuvers are generally more complex, meaning that they can navigate in smaller, denser environments.

“Enabling dynamic flight of small, off-the-shelf quadcopters is a marvelous achievement, and one that has many potential applications,” Yu says. “With additional development, I can imagine these machines being used as probes in hard-to-reach places, from exploring caves to doing search-and-rescue in collapsed buildings.”

Landry, who now works for 3D Robotics in California, is hopeful that other academics will build on and refine the researchers’ work, which is all open-source and available on github.

“A big challenge for industry is determining which technologies are actually mature enough to use in real products,” Landry says. “The best way to do that is to conduct experiments that focus on all of the corner cases and can demonstrate that algorithms like these will actually work 99.999 percent of the time.”

Landry’s work was partially supported by the Siebel Scholars Foundation, while Majumdar’s work was supported by a grant from the Office of Naval Research.

Papers

• Aggressive quadrotor flight through cluttered environments using mixed integer programming”
• Funnel Libraries for Real-Time Robust Feedback Motion Planning

If you liked this post, you may also be interested in:

• “Shrinking bull’s-eye” algorithm speeds up complex modeling from days to hours
• The Master Algorithm, with UW’s Pedro Domingos
•  Quadrocopter failsafe algorithm: Recovery after propeller loss
• ‘Tailsitter’ flying robot hovers and recovers easily thanks to new algorithm
• Robots Podcast #194: Embodied quadrotors, with Davide Scaramuzza

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Students watch CSAIL researchers show off a “garden” made of dozens of distinct origami robots that move and change color to demonstrate complex programming algorithms. Photo: Jason Dorfman/CSAIL

By Adam Conner-Simmons | CSAIL

Last Friday, MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) hosted 150 local high school students for its second annual “Hour of Code” event, tied to the international initiative focused on getting kids interested in programming.

Researchers showed off robots, 3-D-printing technology, and other projects to math and computer science students from schools throughout the greater Boston area, including Cambridge, Charlestown, Roxbury, and Somerville.

The event also included a surprise video message from John Green, author of the bestselling young-adult novels-turned-movies “The Fault In Our Stars” and “Papertowns.” Green commended the students on participating the event and elaborated on why coding is important.

“I cannot emphasize enough how much I believe in learning computer science, not least because I am basically a first-grader when it comes to computer literacy,” Green said. “These days STEM education is vital to being able to understand the world around us and interact with it effectively.”

Among the projects on display were “MultiFab,” a 3-D-printer that can print using a record 10 different materials at once; Atlas, a 6-foot-tall, 400-pound humanoid robot that can open doors, climb stairs and drive a car; and a wireless device that can detect, track and identify individuals from the other side of a wall.

CSAIL Director Daniela Rus kicked off the event by inviting three graduate students to speak about how they got involved in programming and why coding is fun, exciting, and important.

“I like to think being able to code is sort of like having a superpower,” Rus said. “Researchers here can see through walls with wireless, create objects that nobody would have ever thought possible, and develop robots that shape-shift like Transformers and fly through trees at 30 miles per hour.”

To date approximately 150 million students have done an hour of programming as part of the Hour of Code, which was launched in 2013 by the organization code.org and takes place during Computer Science Education Week. Companies such as Apple and Microsoft have also jumped onboard with special programs, while last year U.S. President Barack Obama became the first sitting president to write a line of code.

NASA’s “Valkyrie” robot is 6 feet tall and weighs 290 pounds. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory will test and develop the bot for future space missions. Photo: NASA

By Adam Conner-Simons, MIT CSAIL

NASA announced yesterday that MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is one of two university research groups nationwide that will receive a 6-foot, 290-pound humanoid robot to test and develop for future space missions to Mars and beyond.

A group led by CSAIL principal investigator Russ Tedrake will develop algorithms for the robot, known as “Valkyrie” or “R5,” as part of NASA’s upcoming Space Robotics Challenge, which aims to create more dexterous autonomous robots that can help or even take the place of humans “extreme space” missions. (NASA’s challenge is divided into a virtual competition using robotic simulations, and a physical competition using the robot.)

Tedrake’s team, which was selected from groups that were entered in this year’s Defense Advanced Research Projects Agency (DARPA) Robotics Challenge, will receive as much as $250,000 a year for two years from NASA’s Space Technology Mission Directive.

Exploring deep space by bot

NASA says it is interested in humanoid robots because they can help or even replace astronauts working in extreme space environments. Robots like R5 could be used in future missions either as precursor robots performing mission tasks before humans arrive or as human-assistive robots collaborating with the human crew. While R5 was initially designed to complete disaster-relief maneuvers, its main goal is now to prove itself worthy of even trickier terrain: deep-space exploration.

“Advances in robotics, including human-robotic collaboration, are critical to developing the capabilities required for our journey to Mars,” said Steve Jurczyk, associate administrator for the Space Technology Mission Directorate (STMD) at NASA Headquarters in Washington, in a NASA press release. “We are excited to engage these university research groups to help NASA with this next big step in robotics technology development.”

Autonomy for autos, robots and more

As head of CSAIL’s Robot Locomotion Group, Tedrake has extensive experience with autonomous robots. Over the past three years he led a team of more than 20 researchers to develop algorithms for a government competition to get another 6-foot-tall humanoid robot named Atlas to open doors, turn valves, drill holes, climb stairs, scramble over cinder blocks, and drive a car — all in the space of one hour.

Tedrake is also part of CSAIL’s new $25 million Toyota-funded research center for autonomous cars, specifically focused on advanced decision-making algorithms and systems that allow vehicles to perceive and navigate their surroundings safely, without human input. The larger collaboration will be coordinated by Gill Pratt PhD ’89, a former MIT professor who most recently served as program director at DARPA’s Defense Sciences Office.

In addition to Tedrake’s team at CSAIL, NASA also awarded a R5 robot to a team led by Taskin Padir at Northeastern University.

Team’s silicone rubber gripper can pick up egg, CD & paper, and identify objects by touch alone

By Adam Conner-Simons, MIT CSAIL

Robots have many strong suits, but delicacy traditionally hasn’t been one of them. Rigid limbs and digits make it difficult for them to grasp, hold, and manipulate a range of everyday objects without dropping or crushing them.

Recently, CSAIL researchers have discovered that the solution may be to turn to a substance more commonly associated with new buildings and Silly Putty: silicone.

At a conference this month, researchers from CSAIL Director Daniela Rus’ Distributed Robotics Lab demonstrated a 3-D-printed robotic hand made out of silicone rubber that can lift and handle objects as delicate as an egg and as thin as a compact disc.

Just as impressively, its three fingers have special sensors that can estimate the size and shape of an object accurately enough to identify it from a set of multiple items.

“Robots are often limited in what they can do because of how hard it is to interact with objects of different sizes and materials,” Rus says. “Grasping is an important step in being able to do useful tasks; with this work we set out to develop both the soft hands and the supporting control and planning systems that make dynamic grasping possible.”

The paper, which was co-written by Rus and graduate student Bianca Homberg, PhD candidate Robert Katzschmann, and postdoc Mehmet Dogar, will be presented at this month’s International Conference on Intelligent Robots and Systems.

The hard science of soft robots

The gripper, which can also pick up such items as a tennis ball, a Rubik’s cube and a Beanie Baby, is part of a larger body of work out of Rus’ lab at CSAIL aimed at showing the value of so-called “soft robots” made of unconventional materials such as silicone, paper, and fiber.

Researchers say that soft robots have a number of advantages over “hard” robots, including the ability to handle irregularly-shaped objects, squeeze into tight spaces, and readily recover from collisions.

“A robot with rigid hands will have much more trouble with tasks like picking up an object,” Homberg says. “This is because it has to have a good model of the object and spend a lot of time thinking about precisely how it will perform the grasp.”

Soft robots represent an intriguing new alternative. However, one downside to their extra flexibility (or “compliance”) is that they often have difficulty accurately measuring where an object is, or even if they have successfully picked it up at all.

That’s where the CSAIL team’s “bend sensors” come in. When the gripper hones in an object, the fingers send back location data based on their curvature. Using this data, the robot can pick up an unknown object and compare it to the existing clusters of data points that represent past objects. With just three data points from a single grasp, the robot’s algorithms can distinguish between objects as similar in size as a cup and a lemonade bottle.

“As a human, if you’re blindfolded and you pick something up, you can feel it and still understand what it is,” says Katzschmann. “We want to develop a similar skill in robots — essentially, giving them ‘sight’ without them actually being able to see.”

The team is hopeful that, with further sensor advances, the system could eventually identify dozens of distinct objects, and be programmed to interact with them differently depending on their size, shape, and function.

How it works

Researchers control the gripper via a series of pistons that push pressurized air through the silicone fingers. The pistons cause little bubbles to expand in the fingers, spurring them to stretch and bend.

The hand can grip using two types of grasps: “enveloping grasps,” where the object is entirely contained within the gripper, and “pinch grasps,” where the object is held by the tips of the fingers.

Outfitted for the popular Baxter manufacturing robot, the gripper significantly outperformed Baxter’s default gripper, which was unable to pick up a CD or piece of paper and was prone to completely crushing items like a soda can.

Like Rus’ previous robotic arm, the fingers are made of silicone rubber, which was chosen because of its qualities of being both relatively stiff, but also flexible enough to expand with the pressure from the pistons. Meanwhile, the gripper’s interface and exterior finger-molds are 3-D-printed, which means the system will work on virtually any robotic platform.

In the future, Rus says the team plans to put more time into improving and adding more sensors that will allow the gripper to identify a wider variety of objects.

“If we want robots in human-centered environments, they need to be more adaptive and able to interact with objects whose shape and placement are not precisely known,” Rus says. “Our dream is to develop a robot that, like a human, can approach an unknown object, big or small, determine its approximate shape and size, and figure out how to interface with it in one seamless motion.”

This work was done in the Distributed Robotics Laboratory at MIT with support from The Boeing Company and the National Science Foundation.

RELATED

Paper: “Haptic Identification of Objects using a Modular Soft Robotic Gripper”

Bianca Homberg

Daniela Rus

Distributed Robotics Lab

[article on csail.mit.edu]

In a surprise move today, Toyota held a press conference (see video below) announcing a substantial investment in robotics and AI research to develop “advanced driving support” technology, with former Program Manager of DARPA’s DRC Gill Pratt directing the overall project as Executive Technical Advisor. Toyota will allocate USD$50M over the next five years in a partnership with MIT’s CSAIL (headed by Daniela Rus) and Stanford’s SAIL (headed by Fei-Fei Li) to develop research facilities in Stanford and Cambridge.

Two main themes from the press conference underline the tone of this effort:

First,  Toyota doesn’t refer to autonomous cars but to ‘advanced driving support’ . And this makes sense because, unlike Google (with its fully autonomous car), Toyota’s origins are in car manufacturing and not in software development. Car manufacturers have for some time been introducing ever rising levels of autonomy in their cars and Toyota is following that vector. There are legal advantages to having the driving be ultimately responsible for the car’s movements and therefor any possible accidents; when a vehicle doesn’t respond as expected, a costly product liability suit could follow, as Toyota well knows. Thus what appears at one level to be a more conservative approach to autonomy on the road may in essence be a way to bypass potential legal nightmares and bring autonomous driving technology to the market sooner.

Kiyotaka Ise, Toyota Motor Corporation Senior Managing Officer, repeatedly stated hybrid cars as an example of a specific technology that Toyota pioneered (along with Honda) and brought to market even while it was borderline unprofitable due to its large cost at the time. This offers a possible hint of what may follow.

The second interesting point made clear during Toyota’s Q&A is that this project is only part of the auto manufacturer’s investment in the field. $50M is a lot of money, but for a profitable car maker the size of Toyota, it isn’t much. It looks like Toyota wants to establish a strong presence in the US by collaborating with two of the best research centers in the world (the budget will be equally split between the two universities) … but being a truly global company, it is developing other projects elsewhere as well. It’s worth mentioning that almost all large car manufacturers have similar research projects, and they usually collaborate with research institutes, universities or with each other.

In this respect, Toyota’s decision to bring on Pratt as the project lead appears astute. Pratt’s high visibility role as Program Manager of DARPA’s DRC has left him with significant experience managing and promoting large, big-budget projects, and has provided him with deep connections into US research labs.

You can read Toyota’s press release here for all the details. Automaton has also an excellent coverage of the story (see Toyota Announces Major Push Into AI and Robotics, Wants Cars That Never Crash) and says they will be releasing more in-depth stories about the deal early next week. You can watch the full press conference below:

In this episode, Audrow Nash interviews John Romanishin from MIT, about his modular robotics project ‘M-Blocks’. M-Blocks are small cubes (5 cm on a side) that have no external actuators, yet they manage to move and even jump. They do this by rotating an internal mass at high speeds then stopping that mass suddenly, which transfers inertia to the cube causing it to move. The rotating mass can change which plane it’s spinning in, allowing the cube to move in any direction.  By combining this inertial actuator with permanent magnets, M-Blocks can move over similar robots (or more M-Blocks) and precisely line up. The future of this project is best put by John, who says, “We want hundreds of cubes, scattered randomly across the floor, to be able to identify each other, coalesce, and autonomously transform into a chair, or a ladder, or a desk, on demand.” 

John Romanishin
John Romanishin is currently a graduate student studying mechanical engineering and researching self-reconfigurable modular robots at the Distributed Robotics Laboratory led by Daniela Rus at MIT.

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• John Romanishin’s website

The team, comprised of Daniela Rus, Professor of Electrical Engineering and Computer Science and Director of CSAIL, Cagdas Onal, Assistant Professor of Mechanical Engineering at the Worcester Polytechnic Institute, and Andrew Marchese, a doctoral candidate in engineering at MIT, created the robot to be autonomous. This means it has all the necessary sensing, actuation and computation on board. Its flexible body is made of silicone rubber. The robot fish is able to swish from side to side as onboard gas inflates and deflates different parts of its body. This means that the robot is flexible and can maneuver like a real fish—making a full C-turn with its body in just 100 milliseconds.

According to Rus, this type of soft robot is inherently safe, saying “As robots penetrate the physical world and start interacting with people more and more, it’s much easier to make robots safe if their bodies are so wonderfully soft that there’s no danger if they whack you.”

The fish can perform 20-30 agile escape maneuvers before its carbon dioxide canister runs out. The team is working on an updated version that will be able to swim for around 30 minutes using water to inflate its interior channels rather than carbon dioxide.

In the not so distant future, the fish-bot could be put to use for covert science missions where it might be able to infiltrate schools of real fish to collect data about their behavior.  

The field of soft robotics holds great potential for the development of smart machines that can adjust their shape and size to fit variable environments and interact with living things without causing them harm. Possible applications include: mine detection, assistive healthcare, search and rescue missions and space instrument repair.

Evan Ackerman, writing for IEEE Spectrum’s Automaton blog, says

Researchers at MIT CSAIL have decided that slow and obstacle-free flight is boring, so they’ve come up with a way to get MAVs navigating at high speed, indoors, around obstacles, without needing motion tracking or GPS or beacons or any of that nonsense. All they need is a little aircraft that can carry a planar laser rangefinder, an IMU, and a pre-existing 3D occupancy map that the MAV can localize itself in.

This research has been conducted by the Robust Robotics Group (RRG), led by Nicholas Roy. A paper explaining it in detail was presented at ICRA by graduate student Adam Bry. A similar video using a quadrotor (below) appears on the personal page of RRG Research Scientist Stefanie Tellex, which is worth a visit for the cat video she’s also posted! (IMU = Inertial Measurement Unit)

A Voice-Commandable Robotic Helicopter…