Many hope to be able to solve robotics (and thus car) problems with just cameras. While LIDAR is going to become cheap, it is not yet, and cameras are much cheaper. I outline many of the trade-offs between the systems in my article on cameras vs lasers. Everybody hopes for a research breakthrough or computer vision breakthrough to make vision systems reliable enough for safe operation.

The Michigan lab’s approach is a special machine vision one. They map the road in advance in 3D and visible light by using a mapping car equipped with lots of expensive LIDAR and other sensors. They build a 3D representation of the road similar to what you need for a video game engine, and from that, with the use of GPUs, they can indeed create a 2D image of what a camera should see from any given point.

The car goes out into the world and its actual camera delivers a 2D frame of what it sees. Their system then compares that with generated 2D images of what the camera should see until it finds the closest match. Effectively, it’s like you looking out a window and then going into a video game and wandering around looking for a place that looks like what you see out that window, and then you know where the window is.

Of course it is not “wandering,” and they develop efficient search algorithms to quickly find the location that looks most like the real world image. We’ve all seen video games images, and know they only approximate the real world, so nothing will be an exact match, but if the system is good enough, there will be a “most similar” match that also corresponds with what other sensors, like your GPS and your odometer/dead reckoning system, tell you about where you probably are.

Localization with cameras has been done before, and this is a new approach taking advantage of new generations of GPUs, so it’s interesting. The big challenge is simulating the lighting, because the real world is full of different lighting, high dynamic range, and shadows. The human system has no problem understanding a stripe on the road as it moves through the shadow of a tree, but computer systems have a pretty tough time with that. Sun shadows can be mapped well with GPUs, but shadows from things like the moving limbs of trees are not possible to simulate, as are the shadows of other vehicles and road users. At night, light and shadows come from car headlights and urban lights. The team is optimistic about how well they will handle these problems.

The much larger challenge is object perception. Once you have a simulation of what the camera should see, you can notice when there are things present that are not in the prediction — like another car or pedestrian, or a new road sign. (Right now their system mostly is looking at the ground.) Once you identify the new region, you can attempt to classify it using computer vision techniques, and also by watching it move against the expected background.

This is where it gets challenging, because the bar is very high. To be used for driving it must effectively always work. Even if you miss 1 pedestrian in a million you have a real problem because there are billions of pedestrians encountered by a billion drivers every day. This is why people love LIDAR — if something (other than a mirror or sheet of glass) sufficiently large is sufficiently close you, you’re going to get laser returns from it, and not from what’s behind it. It has the reliability number that is needed.
The challenge of vision systems is to meet that reliability goal.

This work is interesting because it does a lot without relying on AI “computer vision” techniques. It is not trying to look at a picture and recognize a person. Humans are able to look at 2D pictures with bizarre lighting and still tell you not just what the things in the picture are, but often how far away they are and what they are doing. While we can be fooled in a 2D image, once you have a moving dynamic world, humans are, generally reliable enough at spotting other things on the road. (Though of course, with 1.2 million dead each year, and probably 50 million or more accidents, the majority because somebody was “not looking,” we are far from perfect.)

Some day, computer vision will be as good at recognizing and understanding the world as people are — and in fact surpass us. There are fields (like identifying traffic signs from photos) where they already surpass us. For those not willing to wait until that day, new techniques in perception that don’t require full object understanding are always interesting.

I should also point out that while lowering cost is of course a worthwhile goal, it is a false goal at this time. Today, maximal safety is the overriding goal, and as such, nobody will actually release a vehicle to consumers without LIDAR just to save the estimated 2017 cost of LIDAR, which will be sub-$500. Only later, when cameras get so good they completely replace LIDAR safety capabilities for less money would people release such a system to save cost. On the other hand, improving cameras to be used together with LIDAR is a real goal; superior safety, not lower cost.

A version of this article originally appeared on robocars.com. Want to learn more about the the University of Michigan research cited in this article? Check out the research paper by Ryan Wolcott and Ryan Eustice (Best Paper Award at IROS 2014!), as well as Wolcott’s IROS 2014 Webcam research pitch: 

Full transcript below.

In this episode, Sabine Hauert speaks with Travis Deyle, about his IROS-nominated work on RFID tags, his blog Hizook, and the career path that brought him from academia, to founding his own start-up, and finally working for Google[x].

For his PhD at Georgia Tech with Dr. Charles C. Kemp, Deyle helped robots find household objects by tagging them with small Band-Aid-like Ultra High Frequency (UHF) Radio-Frequency Identification (RFID) labels. The tags allowed robots to precisely identify tagged objects. Once identified, the robots would follow a series of simple behaviors to navigate up to the objects and orient towards them.

Compared to vision and lasers, RFID can detect objects that are hidden while providing precise information and identification. This could allow a robot to find a bottle of medication in a cupboard, and make sure it’s the correct medication, before bringing it to a person. Furthermore, the technology can scale to large numbers of objects, and be used to map their location in the environment.

In the future, such tags augmented with better energy, sensing and computation capabilities could form the basis of the Internet of Things and provide a smart environment for robots to interact with.

Travis Deyle

Travis Deyle earned a PhD in Fall 2011 from Georgia Tech’s School of Electrical and Computer Engineering (ECE). His PhD with Dr. Charles C. Kemp at the at Healthcare Robotics Lab was entitled, “Ultra High Frequency (UHF) Radio-Frequency Identification (RFID) for Robot Perception and Mobile Manipulation.”

After his PhD, Deyle worked with Dr. Matt Reynolds as a postdoc researcher at Duke University where he focused on a software-defined radio receiver to decode (in real-time) the high-speed biotelemetry signals reflected by a custom neuro-telemetry chip. This system was designed to capture high-fidelity neural signals from a dragonfly in flight — aka, a “cyborg dragonfly”.

He then co-founded the successful company Lollipuff.com: an online auction site dedicated exclusively to women’s designer clothes and accessories.

Deyle currently works at Google[x] where he was part of the team that made the “smart contact lense” to measure tear glucose levels which was recently licensed to Novartis.

He also founded the well know blog Hizook.com, a robotics website for academic and professional roboticists.

Links:

• Download mp3 (9.8MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS
• Travis Deyle’s website
• Hizook

Source: Peter Corke

In this episode, Audrow Nash interviews Peter Corke from Queensland University of Technology, about computer vision – the subject of his plenary talk at IROS 2014 (link to slides below). He begins with a brief history of biological vision before discussing some early and more modern implementations of computer vision. Corke also talks about resources for those interested in learning computer vision, including his book, Robotic Vision & Control, and a massively open online course (MOOC) that he plans to release in 2015. 

Peter Corke
Peter Corke joined Queensland University of Technology at the start of 2010 as a Professor of Robotic Vision. Now he’s also director of the ARC funded Centre of Excellence for Robotic Vision. Peter is known for his research in vision-based robot control, field robotics and wireless sensor networks. He received a B.Eng and M.Eng.Sc. degrees, both in Electrical Engineering, and a PhD in Mechanical and Manufacturing Engineering, all from the University of Melbourne, Australia.

Links:

• Download mp3 (19.4MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS
• Peter Corke’s Toolbox Website
• Peter Corke’s CyPhy Lab Wiki
• IROS 2014 Plenary Slides: Computer Vision (PDF; includes book recommendations)

SV-ROS team “Maxed-Out” won the IROS 2014 Microsoft Kinect Challenge

One week before the IROS 2014 conference, I received an email from the Maxed-Out team telling me that they were using my open source code RTAB-Map for the Kinect Robot Navigation Contest that would be held during the conference. I am always glad to know when someone is using what I have done, so I said I would answer whatever questions they had before the contest. Since I was already in Chicago to present a paper, I met them in person at the conference and helped them with the algorithm on site before the contest … and good surprise, they won the contest! See their official press release here.

This contest featured robots (equipped only with a Kinect) navigating autonomously across multiple waypoints in a typical office environment. The first part of the contest was to map the environment so that the robot could plan trajectories in it. The robot was driven through the environment to incrementally create a 2D map (like a top view map of an office), using only its sensors. Sensors are not perfect, so maps can become misaligned over time. These errors can be minimized when the robot detects when it comes back to a previously visited area, a process called loop closure detection. This mapping approach is known as Simultaneous Localization And Mapping (SLAM). SLAM is used when the robot doesn’t have a map already built of the environment, and when it doesn’t have access to external global localization sensors (like a GPS) to localize itself. The mapping solution chosen by the SV-ROS team was my SLAM approach called RTAB-Map (Real-Time Appearance-Based Mapping).

In this article I share some of the technical details of the RTAB-Map.

The robot

The robot was an Adept MobileRobots Pioneer 3 DX equipped with a Kinect sensor. The up-facing camera was only used by the scoring computer (detecting patterns on the ceiling to localize the robot). The RTAB-Map ROS setup was based on the “Kinect + Odometry + Fake 2D laser from Kinect” configuration. The “2D laser” was simulated using the Kinect and the depthimage_to_laserscan ros-pkg. In this configuration, the robot is constrained to a plane (x,y and yaw).

Source: IROS 2014

Contest environment

The challenge was held in the exhibit room in the Palmer House Hotel. Since RTAB-Map’s loop closure detection is based on visual appearance of the environment, the repetitive texture on the carpet made it very challenging. When there were only discriminative visual features on the floor (like a long hall), loop closures could be found with images from different locations, thus causing big errors in the map. To avoid this problem, just two minutes before the official mapping run, we set a parameter to ignore 30% of the bottom image for features extraction. We didn’t have the chance to test it before the official mapping run – all we could do was to cross our fingers and hope that no wrong loop closures would be found!

Contest environment

Filtering visual features of the floor

Results

After mapping, no wrong loop closures were found. However, the robot missed a loop closure on the bottom of the map (because the camera was not facing the same direction), causing a wall at the end of the hall. This was one case where, when exploring an unknown environment, it would be better to occasionally do a 360° turn. This would enable the system to see more images at different angles, and would result in more loop closures. However, by using rtabmap-databaseViewer and reprocessing data included in the database, we could detect a loop closure between locations before and after exploring the hall. The result was an aligned bottom corner. There was still a misalignment on the middle-top, but given the time we had, the map was good enough for the navigation part of the contest. Below are results with these remaining errors corrected. The loop closures are shown in red.

a) After first mapping; b) Used for navigation; c) Updated results (after the challenge)

During the autonomous navigation phase, RTAB-Map was set in localization mode with the pre-built map. By detecting “loop closures” with the previous map, the robot is relocalized so it can efficiently plan its trajectories through the environment.

Updated Results

In this section, I will show a parametrization of RTAB-Map that I did after the challenge to automatically find more loop closures. The video above can be reproduced using the data here:

• RTAB-Map database: IROS14-kinect-challenge.db
• RTAB-Map parameters: IROS14-kinect-challenge.ini

You can open RTAB-Map (standalone, not the ros-pkg) using the configuration file above, then set “Source from database” and select the database above. Set Source rate to 2 Hz and press start; this should reproduce the video above. Here the principal parameters modified from the defaults:

// -Filter 30% bottom of the image
// -Maximum depth of the features is 4 meters
Kp/RoiRatios=0 0 0 0.3
Kp/MaxDepth=4

// -Loop closure constraint, computed visually
LccBow/Force2D=true
LccBow/InlierDistance=0.05
LccBow/MaxDepth=4
LccBow/MinInliers=3

// -Refining visual loop closure constraint using 
//   2D icp on laser scans
LccIcp/Type=2
LccIcp2/CorrespondenceRatio=0.5
LccIcp2/VoxelSize=0

// -Disabled local loop closure detection based on time.
// -Optimize graph from the graph beginning, to 
//   have same referential when relocalizing to this 
//   map in localization mode.
RGBD/LocalLoopDetectionTime=false
RGBD/OptimizeFromGraphEnd=false

// -Extract more SURF features
SURF/HessianThreshold=60

// Run this for a description of the parameters:
$ rosrun rtabmap rtabmap --params

The following pictures show a comparison of the results with and without loop closures:

	Odometry Only	With Loop Closures
2D Map
3D Cloud
2D Map + 3D Cloud

3D Cloud (download here: cloud.ply)

3D Cloud + 2D Map (saved with map_server: map.pgm and map.yaml)

Conclusion

SV-ROS team has created a great solution for autonomous navigation and they won the IROS 2014 Kinect Navigation Contest. I’m glad that my algorithm RTAB-Map was part of it. It was really nice to meet the team – they were very motivated, enthusiastic and all their efforts were rewarded. This post has provided a technical description of the mapping approach used by the team and shows some promising results using visual features for loop closure detection on a robot equipped only with a Kinect.

Source: Microsoft Research

On the surface, the challenge sounds rather easy – at least for a human. Given a cluttered cafeteria-style room as shown on the right, teams would have to program their robot to navigate autonomously to five specific locations all while avoiding tables, chairs, sofas, people and other objects. Each team would be given a limited time to first map the room with their robot and make note of the five locations. Each location was designated by a marker on the ceiling which would never be visible to the robot itself but could be used by the programmers during mapping to know when the robot was at a target location.

During the test run, the five location numbers would be given in a specific order to each team and the challenge was for the robot to move autonomously (i.e. without further control from the programmers) to each of these locations in the correct sequence and as quickly as possible without running into things or getting stuck. To make the task even more challenging (seemingly impossible when we first read about it), during the interval between the mapping and test phases, furniture and other objects could be moved, added or removed from the room. As if that weren’t enough, a number of people would be walking around the room during the test phase periodically crossing the robot’s path or even standing directly in its way for up to 30 seconds.

Source: Microsoft Research

All teams would be given the same robot, a Pioneer 3-DX (shown on the left) from Adept Mobile Robots. The contest was co-sponsored by Microsoft who provided the Kinect for Windows depth camera located on the vertical post attached to the back of the robot and facing directly forward. The camera was the only sensor that would be available to the programmers. There were no sonar or IR sensors and no laser scanners. So all mapping, navigation, and obstacle avoidance would have to be based on vision alone.

To understand why this task seemed nearly impossible, it helps to keep in mind two important points: (a) the robot’s camera was only 18 inches (46 cm) off the ground and (b) the most widely used automated mapping technique (SLAM), typically relies on a laser scanner with a wide field of view (e.g. 180 degrees) while the Kinect has a visual field of view of only 45 degrees. So unlike a person’s view of the room, the robot would be looking at objects from roughly the same height as someone crawling on their knees with a forward facing cone tied to their head blocking all peripheral vision. And even if we did have a proper laser scanner, imagine what it would see when sweeping across the room: basically just a large number of narrow chair legs and table pedestals that could be moved around anyway before the testing phase.

Even so, we would at least have the entire RGB video stream from the Kinect camera as well as its depth data. So what would a human do with such data while moving around the room trying to remember the five target locations?

Psychologists and biologists have known for some time that people and animals use visual landmarks to help localize themselves in a given environment, and fortunately for us, robotics researchers have been working on visual mapping and localization (“Visual SLAM” or “RGBD-SLAM”) for a number of years now. The most promising of these efforts appears to be the work coming out of the IntRoLab at the Université de Sherbrooke in Quebec. In particular, Mathieu Labbé has developed a Real-Time-Appearance-Based Mapping algorithm (RTAB-map) that stitches together visual features from an RGB-Depth camera like the Kinect and creates a truly amazing three-dimensional representation of rooms or other surroundings as shown in the video below:

Armed with RTAB-map, the SV-ROS team managed to borrow a Pioneer robot ahead of time and got to work programming all the control scripts, launch files, and configuration parameters needed to handle the requirements of the contest. In the meantime, Pi Robot had joined the party and was able to test the mapping procedure using his own Kinect camera. I also created a simulated cafeteria setting in Gazebo so that we could run a virtual mock-up of the contest over and over again with different navigation parameters to determine which settings resulted in the best performance.  We stayed in contact via e-mail and were trying out new ideas right up to the last minute.

In the end, four of the team members (Greg Maxwell, Steve Okay, Ralph Gnauck and Girts Linde) flew to Chicago several days ahead of the event and fine tuned the robot’s behavior even further. They even had the good fortune to meet up with Mathieu Labbé who gave a paper on RTAB-map at the IROS conference. Of the six teams that entered the contest, only one other team made as many waypoints as our robot (3 out of 5 locations), but our robot did the course twice as fast so we came out on top. The fourth waypoint was at the end of a long featureless hallway that completely confused the vision-based localization methods used by RTAB-map and was not a situation that we anticipated or tested. At least we know better for next time!

Robohub will be bringing you a write up about RTAB by Mathieu Labbé in the coming days. You can also read the official press release from the SV-ROS user’s group on the ROS.org newsfeed.

Source: SV-ROS

Ryan Wolcott University of Michigan
Visual localization within LIDAR maps for automated urban living
Winner of the IROS 2014 Best Student Paper award!! 

More info

Michael T. Tolley Harvard University
An untethered jumping soft robot

See also on IEEE Automaton
More info

Tamás Haidegger Óbuda University
Robust fixed point transformation based design for MRAC of a modified TORA system

More info

Mustafa Mukadam University of Illinois at Urbana-Champaign
Quasi-static manipulation of a planar elastic rod using multiple robotic grippers

Check out all our IROS14 coverage.

Video courtesy of Per Sjöberg.

Sampriti Bhattacharyya MIT
Compact, Tetherless ROV for In-Contact Inspection of Underwater Structures

Josh Lane Purdue University
MOTHERSHIP: A Serpentine Limb/Tread Hybrid Robot

More info

Yanzhe Cui Purdue University
RrFrESH: A Self-Adaptation Framework to Support Fault Tolerance in Robot

More info

Ying Lu Rensselaer Polytechnic Institute
On the Convergence of Fixed-point Iteration in Solving Complementarity Problems Arising in Robot Locomotion and Manipulation

More info

Fei Chen Instituto Italiano di Tecnologia
A Study on Data-Driven In-Hand Twisting Process Using a Novel Dexterous Robotic Gripper for Assembly Automation

More info

Leif Jentoft Righthand Robotics
Not an official IROS14 paper, but a cool pitch none-the-less! Righthand Robotics was one of two hands selected for DRC Track A robots.

More info

Check out all our IROS14 coverage.

The IROS 2014 Industry Forum brought together entrepreneurs, researchers, venture capitalists and funding agencies from North America, Europe, Asia and Australia to shed perspective on the above questions and engage in open dialogue about some of the pitfalls of commercializing robotics technology. Here are the 10 best quotes from the event …

1. “Testing nothing and testing everything are equally lazy.”
– Ryan Gariepy, Clearpath Robotics

Gariepy refers to the extremes some startups take before launching a new product. He instead suggests evaluating the technologies, processes and people that can be reused efficiently, then taking a decisive step to engage your customer.

2. “Competition is not other companies or technologies, it’s the status quo.”
– Ryan Gariepy, Clearpath Robotics

Academics, consumed with dissecting the research of predecessors, colleagues and other institutions, then publishing novel work, naturally look to see what others of doing in the field to gauge their own progress. To academics looking to commercialize research, Gariepy says it’s important to know what’s out there but also to run your own race. When launching a new product, you need to focus on market first, then technology.

3. “Cheaply eliminate bad business models fast.” 
– Lee Redden, Blue River Technology

Before building their first product, Redden and team hired five part-time interns to talk with potential customers, and eliminated five poor business models in less than a month.  CarrotBot, a weeder for carrot farms, came about after talking with an IT procurement manager at a carrot farm who had struggled with finding the right technology for their particular problem. Redden also notes that if you want honest feedback from potential customers, make them feel comfortable by sharing the flaws of your product upfront and don’t show up with a perfect prototype – most people are hard-wired to protect the feelings of those they interact with, so you have to disarm them to get honest answers. “Cheaply eliminate bad business models fast.” – Lee Redden, Blue River Technology 

4. “Sometimes what the company wants and what the community wants are not perfectly aligned.”  
– Brandon Basso, 3D Robotics

Balancing of the vision of the company with the wants of an open-source community can be tricky. The 3D Robotics (3DR) community wants breadth – more hardware, sensor and platform support – whereas the company wants depth to support the community while developing native system apps and more hardware solutions. Challenges arise when a growing company has to execute on set product and revenue objectives defined by management (and in 3DR’s case, venture capital stakeholders) while integrating the community’s feedback into their product suite. Long-term, this relationship creates a virtuous cycle. The community provides 3DR with an on-demand workforce, tech support via developer list, rapid exploration of new opportunities and a huge beta test group. 3DR gives back to the community through cheaper and faster hardware, expertise, capital, support and worldwide distribution.

5. “The robot revolution will be an evolution.”
– Christopher Parlitz, SCHUNK

According to Parlitz, the integration of components is the new challenge in robotics. Without standards in place for mechanical and electrical components or transfer of data, creating intelligent systems will take time. He talks of Programmable Logic Controllers (PLCs) and ROS as two solutions for integrating hardware components, each with its list of pros and cons. As new integrated intelligent solutions start working robustly, they will no longer be considered “robots”, making the progression much more of an evolution than a revolution. “The robot revolution will be an evolution.” – Christopher Parlitz, @SCHUNKInc 

6. “If I lose control of my startup, my invention won’t wind up at a fire sale.” 
– Michael Peshkin, Northwestern University

IP. IP. IP. Intellectual property dominated this year’s industry discussion. Each university’s tech transfer office has its own rules – some grant both faculty and student inventors full ownership, others to students but not to faculty, and then there are jointly-owned inventions between university and industry (Renaud Champion, a venture capital partner at Robolution warns researchers against these). Emotions and the perceived value of owning intellectual property can often outweigh its real monetary and practical benefits. Peshkin speaks to this point and outlines six key reasons why he prefers Northwestern University’s technology transfer office to own his patents:

• They pay the expenses, which can cost hundreds of thousands of dollars
• If they don’t want to pay, they unambiguously turn the rights over
• Future investors shouldn’t worry about IP ownership
• They take a reasonable percent of royalties and/or equity
• They won’t ever sell a patent
• “If I lose control of my startup, my invention won’t end up at a fire sale” – Michael Peshkin, Northwestern University 

7. “If there are people with pitchforks at your door, you’re probably doing something right.” 
– Shahin Farshchi, Lux Capital

In addition to IP, regulations (or lack thereof) of certain robotics products create an uncertain environment for commercializing technology. Farshchi says focus on building a company with a clear value proposition that customers want. If regulatory bodies want to speak with you, it’s probably because you’ve taken enough market share to draw attention. And as a first mover, you’ll more likely have the opportunity to shape regulation which will put you in a favorable position going forward.

8. “Good research doesn’t necessarily mean a good product.”
– Ayanna Howard, Zyrobotics

People buy things that are useful to them and shifting focus from product specs to selling a value proposition can stumble some researchers. That’s why Howard says it’s important to be self-aware and know when it’s time to bring in outside expertise. She sees the quality of management as being just as important – if not more important – than the technology itself.

9. “If I have competition, there’s a market, if there’s a market, there’s an industry, and if there’s an industry, there’s liquidity.”
– Renaud Champion, Robolution Capital

Competition is not always a good thing, but in a nascent market for robotics products, it can be great. Competition means there’s enough market demand for several products to compete with one another and from a venture capitalist’s point of view that means there’s room for growth and potential exit opportunities.

10. “I have a dream. I have a vision. Why not me?” 
– Renaud Champion, Robolution Capital

At some point, starting a new business takes a leap of faith. Dreams can take a long time to manifest. Finding the right (or sometimes good-enough) answers to build a solid vision, a product, a team, a brand and convince customer and investors that your product can bring value to the community is tedious and was compared to a rollercoaster ride by many of entrepreneurs at the forum.  But this process, which almost always transforms the entrepreneur (and if they’re lucky, an entire industry), often starts with a simple question: “Why not me?” “I have a dream. I have a vision. Why not me?” – Renaud Champion, Robolution Capital 

Follow all of our IROS 2014 coverage here.

The 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014) is on in Chicago, and members of the robotics research community have descended on the Windy City. This year the organizing committee experimented with a new 3-minute presentation format followed by an interactive session. We invited researchers to give their “pitch” in front of our IROS cam … below are just some of the great pitches we came across in the interactive sessions. Watch out for more IROS Cam videos coming soon!

Guido De Croon European Space Agency, TU Delft
Crowdsourcing as a methodology to obtain large and varied robotic data sets

More info

Felix Berkenkamp University of Toronto
Learning-based Robust Control: Guaranteeing Stability while Improving Performance

More info

Daniel Lofaro George Madison University
A lightweight, cross-platform, multiuser robot visualization using the cloud

More info

Franziska Meier University of Southern California
Efficient Bayesian Local Model Learning for Control

More info

Patricio J. Cruz Dávalos University of New Mexico
Stable Formation of Groups of Robots via Synchronization

More info

Shame that the missing engineering hero on the cover of IEEE Spectrum’s July issue looks like superMAN , because the IEEE Women in Engineering (WIE) lunch at IROS 2014 was a super gathering of high achievers who are making statements about 6% representation looking more like a glass half full, if you harness the potential. Lynne Parker, for example, is the General Chair of ICRA 2015, with a 50 strong all-female organizing committee for the premier robotics research event .

ICRA is the IEEE Robotics and Automation Society’s flagship conference and is a premier international forum for robotics researchers to present their work. The 2015 conference will be held May 26-30, 2015 at the Washington State Convention Center in Seattle, Washington, USA. Having an all-women committee serves as an important watershed for the robotics community.

Parker said, “There are usually only one or two women involved in conferences and when I ask why so few, people say that there aren’t enough women with experience. So, look at all these women with experience now who can be helping with future committees.” Women might make up only 6% of the global IEEE community but Parker feels that even that presence hasn’t been sufficiently felt. She also encourages women to submit papers or workshops to ICRA 2015 by the deadline of October 1st, 2014 (just around the corner!).

As well as the incredible list of women on the ICRA 2015 committee (below), Robohub published a ’25 women in robotics you need to know about’ list in 2013 and we’ll be publishing a new list for Ada Lovelace Day on October 14, 2014. Ada Lovelace Day celebrates the achievement of women in computing, science, technology and engineering.

The idea is to provide inspiring role models across a wide range of robotics fields. The women on these lists are a fantastic sample from an admittedly small group. I encourage everyone to look further afield when searching for women to meet targets for boards, committees, positions and outreach, rather than expect the same few women to pick up all the slack. (This ’empowerment 101′ applies to any diversity demographic.)

Of course women are not the only ones who can change the demographics of the field, but it definitely helps to see women rising to positions of influence. For example, Lynne Parker has just been appointed Division Director for the NSF’s Information and Intelligent Systems (IIS) Division within the Computer and Information Science and Engineering (CISE) Directorate.

This 3-year role covers overseeing the National Robotics Initiative alongside big data, human-centered computing, and core computer science. Parker is looking not just at internal program management but is also looking ahead to see what are the new directions for the robotics community. “To be somewhat visionary, to the extent that we can be,” said Parker. “For example, we’re an international community. Can we do better at representing that from a funding perspective? That’s a hard challenge, but that does’t mean we can’t look at it.”

In closing, the CISE Directorate enthusiastically welcomes Dr. Parker to NSF and looks forward to working with her to advance the frontiers of knowledge in information and intelligent systems.” Farnam Jahanian, NSF 

We also heard from Ayanna Howard, HumAnS Laboratory and GIT,  and Jeff Trinkle, NSF, NRI and Rensselaer, at the WIE luncheon at IROS 2014. With over 15,000 members around the world, IEEE’s Women in Engineering (WIE) is the largest international professional organization dedicated to promoting women engineers and scientists and inspiring girls around the world to follow their academic interests to a career in engineering. Formed in 1994, the luncheon marks the 20th anniversary of WIE.

https://www.youtube.com/watch?v=pukuQw0Ov0s

ICRA 2015 Organizing Committee

Honorary Chair
Ruzena Bajcsy (University of California, Berkeley)

General Chair
Lynne Parker (University of Tennessee)

Program Chair
Nancy M. Amato (Texas A&M University)

Program Co-Chairs
Dani Kragic (Royal Institute of Technology, KTH)
Hong Qiao (Chinese Academy of Sciences)
Jing Xiao (University of North Carolina, Charlotte)

CEB Editor-in-Chief
Allison Okamura (Stanford University)

Finance Chair
Yi Guo (Stevens Institute of Technology)

Workshops and Tutorials Chair
Monica Anderson (University of Alabama)

Workshops and Tutorials Co-Chairs
Spring Berman (Arizona State University)
Alicia Casals (Technical University of Catalonia)
Yukie Nagai (Osaka University)

Interactive Sessions Chair
Ani Hsieh (Drexel University)

Interactive Sessions Co-Chair
Ming Lin (University of North Carolina, Chapel Hill)

Awards Chair
Lydia Kavraki (Rice University)

Awards Co-Chairs
Jessica Hodgins (Carnegie Mellon University)
Daniela Rus (Massachusetts Institute of Technology)
Carme Torras (Institut de Robòtica i Informàtica Industrial, CSIC-UPC)

Publication Chair
Dawn Tilbury (University of Michigan)

Publicity Chair
Nora Ayanian (University of Southern California)

Publicity Co-Chairs
Raffaela Carloni (University of Twente)
MIhoko Otake (Chiba University)

Exhibitions Chair
Robin Murphy (Texas A&M University)

Exhibitions Co-Chair
Cecilia Laschi (Scuola Superiore Sant’Anna)

Sponsorships Co-Chairs
Manuela Veloso (Carnegie Mellon University)
Maja Mataric (University of Southern California)

Industry Forum Chair
Aude Billard (Ecole Polytechniuque Federale de Lausanne)

Industry Forum Co-Chairs
Dana Kulic (University of Waterloo)
Angelika Peer (TU Munich)
Yuru Zhang (Beihang University)

Developing Countries Outreach Chair
M. Bernardine Dias (Carnegie Mellon University)

Developing Countries Outreach Co-Chairs
Chinwe Ekenna (Texas A&M University)
Ayorkor Korsah (Carnegie Mellon University)

Career Fair Chair
Hadas Kress-Gazit (Cornell University)

Career Fair Co-Chairs
Maren Bennewitz (University of Freiburg)
Jana Koseca (George Mason University)

PhD Forum Chair
Ayanna Howard (Georgia Institute of Technology)

PhD Forum Co-Chairs
Jamie Paik (EPFL)
Hae Won Park (Georgia Institute of Technology)
Xiaorui Zhu (Harbin Institute of Technology)

Competitions Co-Chairs
Sonia Chernova (Worcester Polytechnic Institute)
Manuela Veloso (Carnegie Mellon University)

Travel Awards Chair
Maria Gini (University of Minnesota)

Student Activities Chair
Lydia Tapia (University of New Mexico)

Student Activities Co-Chair
Hanna Kurniawati (University of Queensland)

K-12 Outreach Chairs
Radhika Nagpal (Harvard University)
Claire Tomlin (University of California, Berkeley)

Web Chair
Shawna Thomas (Texas A&M University)

Local Arrangements Chair
Kristy Morganson (University of Washington)

Local Arrangements Co-Chair
Maya Cakmak (University of Washington)

Senior Program Committee

Ruzena Bajcsy (University of California, Berkeley)
Alicia Casals (Technical University of Catalonia)
Bernardine Dias (Carnegie Mellon University)
Maria Gini (University of Minnesota)
Yi Guo (Stevens Institute of Technology)
Ayanna Howard (Georgia Institute of Technology)
Lydia Kavraki (Rice University)
Jana Koseca (George Mason University)
Dani Kragic (Royal Institute of Technology, KTH)
Ming Lin (University of North Carolina, Chapel Hill)
Robin Murphy (Texas A&M University)
Radhika Nagpal (Harvard University)
Allison Okamura (Stanford University)
Katia Sycara (Carnegie Mellon University)
Dawn Tilbury (University of Michigan)
Carme Torras (Institut de Robòtica i Informàtica Industrial, CSIC-UPC)
Manuela Veloso (Carnegie Mellon University)
Jing Xiao (University of North Carolina, Charlotte)
Yuru Zhang (Beihang University)