In this episode, Abate flew to Denver, Colorado, to get a behind-the-scenes look at the future of recycling with Joe Castagneri, the head of AI at Amp Robotics. With Materials Recovery Facilities (MRFs) processing a staggering 25 tons of trash per hour, robotic sorting is the clear long-term solution.

Recycling is a for-profit industry. When the margins don’t make sense, the items will not be recycled. This is why Amp’s mission to use robotics and AI to bring down the cost of recycling and increase the number of items that can be sorted for recycling is so impactful.

Joe Castagneri
Joe Castagneri graduated with his Master of Science in Applied Mathematics, with an undergrad degree in Physics. While still in university, he first joined the team at Amp Robotics in 2016 where he worked on Machine Learning models to identify recyclables in video streams of Trash in Materials Recovery Facilities (MRFs). Today, he is the Head of AI at Amp Robotics where he is changing the economics of recycling through automation.

Nico, Emil, and Moritz founded ReRun with the mission of making powerful visualization tools free and easily accessible for roboticists. Nico and Emil talk about how these powerful tools help debug the complex problem scopes faced by roboticists. Tune in for more.

Nikolaus West
Co-Founder and CEO
Niko is a second-time founder and software engineer with a computer vision background from Stanford. He’s fanatic about bringing great computer vision and robotics products to the physical world.

Emil Ernerfeldt
Co-Founder and CTO
Emil fell in love with coding over 20 years ago and hasn’t looked back since. He’s the creator of egui, an easy-to-use immediate mode GUI in Rust, that we’re using to build Rerun. He brings a strong perspective from the gaming industry, with a focus on great and blazing fast tools.

Links

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Conventional sheet metal manufacturing is highly inefficient for the low-volume production seen in the space industry. At Machina Labs, they developed a novel method of forming sheet metal using two robotic arms to bend the metal into different geometries. This method cuts down the time to produce large sheet metal parts from several months down to a few hours. Ed Mehr, Co-Founder and CEO of Machina Labs, explains this revolutionary manufacturing process.

Ed Mehr

Ed Mehr is the co-founder and CEO of Machina Labs. He has an engineering background in smart manufacturing and artificial intelligence. In his previous position at Relativity Space, he led a team in charge of developing the world’s largest metal 3D printer. Relativity Space uses 3D printing to make rocket parts rapidly, and with the flexibility for multiple iterations. Ed previously was the CTO at Cloudwear (Now Averon), and has also worked at SpaceX, Google, and Microsoft.

Links

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Many people associate prosthetic limbs with nude-colored imitations of human limbs. Something built to blend into a society where people have all of their limbs while serving functional use cases. On the other end of the spectrum are the highly optimized prosthetics used by Athletes, built for speed, low weight, and appearing nothing like a human limb.

As a child under 12 years old, neither of these categories of prosthetics particularly speaks to you. Open Bionics, founded by Joel Gibbard and Samantha Payne, was started to create a third category of prosthetics. One that targets the fun, imaginative side of children, while still providing the daily functional requirements.

Through partnerships with Disney and Lucasfilms, Open Bionics has built an array of imagination-capturing prosthetic limbs that are straight-up cool.

Joel Gibbard dives into why they founded Open Bionics, and why you should invest in their company as they are getting ready to let the general public invest in them for the first time.

Joel Gibbard

Joel Gibbard lives in Bristol, UK and graduated with a first-class honors degree in Robotics from the University of Plymouth, UK.

He co-founded Open Bionics alongside Samantha Payne with the goal of bringing advanced, accessible bionic arms to the market. Open Bionics offers the Hero Arm, which is available in the UK, USA, France, Australia, and New Zealand. Open Bionics is revolutionizing the prosthetics industry through its line of inspiration-capturing products.

Links

• Open Bionics
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The Recycling Industry in the United States is a for-profit industry. They profit from taking recyclable material, refining it, and reselling it to companies at a cheaper price than producing the material from scratch.

Supply and Demand in Recycling

If you look at the demand side of the recycling industry, an array of multi-billion dollar companies like Coca-Cola and PepsiCo are incentivized to buy recycled goods and reduce their materials costs.

On the supply side, ~300 million tons of trash are generated annually in the United States. Estimates suggest that up to 75% of that is recyclable.

On paper, it seems clear that maximizing the amount of trash the US recycles is in everyone’s interest. One issue though, less than a third of the trash ends up recycled.

Areeb, co-founder of Glacier, breaks down the multi-layered reasoning behind why the Recycling industry cannot handle this volume of trash, and what Glacier is doing to address this.

Areeb Malik

Areeb Malik is the Co-Founder of Glacier, and he is on a personal mission to fight climate change and extract value from the $123B worth of recyclables that fill the landfills and oceans. Before founding Glacier, Areeb was a Software Engineer at Facebook, where he used Machine Learning and Computer Vision to build out new product features.

Links

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At ICRA 2022, Benjamin Rosman delivered a keynote presentation on an organization he co-founded called “Deep learning Indaba”.

Deep Learning Indaba is based in South Africa and their mission is to strengthen Artificial Intelligence and Machine Learning communities across Africa. They host yearly meetups in varying countries on the continent, as well as promote grass roots communities in each of the countries to run their own local events.

What is Indaba?

An indaba is a Zulu word for a gathering or meeting. Such meetings are held throughout southern Africa, and serve several functions: to listen and share news of members of the community, to discuss common interests and issues facing the community, and to give advice and coach others.

Benjamin Rosman

Benjamin Rosman is an Associate Professor in the School of Computer Science and Applied Mathematics at the University of the Witwatersrand, South Africa, where he runs the Robotics, Autonomous Intelligence, and Learning (RAIL) Laboratory and is the Director of the National E-Science Postgraduate Teaching and Training Platform (NEPTTP).

He is a founder and organizer of the Deep Learning Indaba machine learning summer school, with a focus on strengthening African machine learning. He was a 2017 recipient of a Google Faculty Research Award in machine learning, and a 2021 recipient of a Google Africa Research Award. In 2020, he was made a Senior Member of the IEEE.

Links

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The team at Retrocausal built a computer vision platform that allows any manufacturer to rapidly set up an activity recognition pipeline to assist with manual assemblies. Using any standard camera pointed at an assembly station, the system can learn the assembly procedure and give real-time feedback.

Deci:

At Deci, they are tackling the problem of taking a Machine Learning model optimized for a single hardware platform and adapting it to work on a different hardware platform.

When developing an ML model running on the edge, a time-consuming development cycle is made to optimize the model to run at peak capacity. However, over time hardware platforms get updated and companies need to make the decision of whether or not to update their hardware and dedicate engineering resources to manually tweak a multitude of settings.

Deci allows companies to upload their models to the cloud, then automatically optimize them for running on a variety of different hardware platforms that they own in their facilities.

StegAI:

Dr. Eric Wengrowski co-founded StegAI after publishing a paper in CVPR 2019 on embedding “fingerprints” that are embedded in images and videos that are invisible to the human eye, but detectable by their software.

Their product modulates the pixels in an image in such a way that even after compressing, resizing, or printing out the images, their deep learning algorithms will still be able to detect the origin of the image.

This powerful tool is used by social media firms to protect against potential copyright infringement when users upload content to their platforms

A lot of times on our podcast we dive into startups and smaller companies in robotics. Today’s talk is unique in that Brady Watkins gives us insight into how a big company like Softbank Robotics looks into the Robotics market.

we think scale first, (the) difference from a startup is our goal isn’t to think what’s the first 10 to 20, but we need to think what’s the first 20,000 look like. – Brady Watkins

Brady Watkins

Brady Watkins is the President and General Manager at Softbank Robotics America. During his career at Softbank, he helped to scale and commercialize Whiz, the collaborative robot vacuum designed to work alongside cleaning teams. Watkins played a key role in scaling the production to 20,000 units deployed globally.

Prior to his time at SBRA, Watkins was the Director of Sales, Planning, and Integration at Ubisoft, where he held several positions over the course of 10 years.

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Marine environments present a unique set of challenges that render several technologies that were developed for land applications completely useless. Communicating using sound, or at least as people use sound to communicate, is one of them.

Michael Fulton tackles this challenge with his presentation at ICRA 2022 by using body language to communicate with an AUV underwater. Tune in for more.

His poster can be viewed here.

Michael Fulton

Michael Fulton is a Ph.D. Candidate at the University of Minnesota Twin Cities. His research focuses primarily on underwater robotics with a focus on applications where robots work with humans. Specifically, human-robot interaction and robot perception using computer vision and deep learning, with the intent of creating systems that can work collaboratively with humans in challenging environments.

At ICRA 2022, the researchers weren’t the only ones working with cutting-edge technology. We spoke to the exhibitors to get real-life demos of their products.

Tangram Vision

Tangram Vision is a hardware-agnostic sensor fusion platform. It streamlines the development and deployment of critical sensor infrastructure like calibration, fusion, and monitoring for any number of cameras, depth sensors, LiDAR, radar, and IMU. Their co-founder, Adam Rodnitzky, walks us through their sensor fusion platform.

FLX Solutions

Matt Bilsky, Founder and CEO of FLX Solutions gives us a live demo of their robot, the FLX BOT. Matt Bilsky applied his Ph.D. in Mechanical Engineering to create a novel, highly compact robot that is designed to reach and inspect parts of a building that a human cannot reach. The FLX BOT is one inch in diameter and is made of modular links that can be attached one after the other to extend the reach and degrees of freedom of the robot.

Exyn Technologies

Exyn Technologies specializes in aerial robotics, drone swarms, multi-modal sensor fusion, 3D mapping, obstacle avoidance, and autonomous navigation & planning.

Exyn’s focus is on developing software for aerial robots so they can operate in GPS-denied environments, without human control, prior information, or pre-existing infrastructure (e.g. no motion capture system).

Pollen Robotics

Pollen Robotics developed the robot, Reachy, an open-source humanoid robot with a quirky appearance. Reachy’s primary userbase is researchers studying fields such as teleoperation. Using mixed autonomy, Reachy is able to be teleoperated by humans, while still having onboard intelligence to autonomously infer the actions you want it to do.

Brandon Gilles, Founder and CEO of Luxonis, tells us his story about how Luxonis designed one of the most versatile perception platforms on the market.

Brandon took the lessons learned from his time at Ubiquiti, which transformed networking with network-on-a-chip architectures, and applied the mastery of embedded hardware and software to the OAK-D camera and the broader OAK line of products.

To refer to the OAK-D as a stereovision camera tells only part of the story. Aside from depth sensing, the OAK-D leverages the Intel Myriad X to perform perception computations directly on the camera in a highly power-efficient architecture.

Customers can also instantly leverage a wide array of open-source computer vision and AI packages that are pre-calibrated to the optics system.

Additionally, by leveraging a system-on-a-module design, the Luxonis team easily churns out a multitude of variations of the hardware platform to fit the wide variety of customer use cases. Tune in for more.

Brandon Gilles

Brandon Gilles is the Founder and CEO of Luxonis, maker of the OAK-D line of cameras. Brandon comes from a background in Electrical and RF Engineering. He spent his early career as a UniFi Lead at Ubiquiti, where his team helped bring Ubiquiti’s highly performant and power-efficient Unifi products to market.

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Dr. Sihao Sun discusses his award-winning research in the area of controlling the flight of a drone when faced with a sudden rotor failure.

Typical research in this area addressed the case where one of the four rotors in a quadrotor suddenly, spontaneously stops working. This previous research does not take into full account real-life scenarios where rotor failure is common. This includes collisions with other drones, walls, birds, and operating in degraded GPS environments.

Dr. Sihao Sun

Dr. Sihao Sun is a postdoctoral research assistant at the Robotics and Perception Group (RPG) in University of Zurich directed by Prof. Davide Scaramuzza. Currently, he is working on control and perception for aerial robots (drones).

In December 2020, he received his PhD degree in Aerospace Engineering from the Control and Simulation Group of Delft University of Technology. His works on quadrotor fault-tolerant flight control have been featured by reputable media, such as IEEE Spectrum.

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Underwater Autonomous Vehicles face challenging environments where GPS Navigation is rarely possible. John McConnell discusses his research, presented at ICRA 2022, into fusing overhead imagery with traditional SLAM algorithms. This research results in a more robust localization and mapping, with reduced drift commonly seen in SLAM algorithms.

Satellite imagery can be obtained for free or low cost through Google or Mapbox, creating an easily deployable framework for companies in industry to implement.

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Sebastian Scherer from CMU’s Airlab gives us a behind-the-scenes demo at ICRA of their Autonomous Flight Control AI. Their approach aims to cooperate with human pilots and act the way they would.

The team took this approach to create a more natural, less intrusive process for co-habiting human and AI pilots at a single airport. They describe it as a Turing Test, where ideally the human pilot will be unable to distinguish an AI from a person operating the plane.

Their communication system works parallel with a 6-camera hardware package based on the Nvidia AGX Dev Kit. This kit measures the angular speed of objects flying across the videos.

In this world, high angular velocity means low risk — since the object is flying at a fast speed perpendicular to the camera plane.

Low angular velocity indicates high risk since the object could be flying directly at the plane, headed for a collision.

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Yu Sun, Professor of Computer Science and Engineering at the University of South Florida, created and organized the Robotic Grasping and Manipulation Competition. Yu talks about the impact robots will have in domestic environments, the disparity between industry and academia showcased by competitions, and the commercialization of research.

Yu Sun

Yu Sun is a Professor in the Department of Computer Science and Engineering at the University of South Florida (Assistant Professor 2009-2015, Associate Professor 2015-2020, Associate Chair of Graduate Affairs 2018-2020). He was a Visiting Associate Professor at Stanford University from 2016 to 2017, and received his Ph.D. degree in Computer Science from the University of Utah in 2007. Then he had his Postdoctoral training at Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA (2007-2008) and the University of Utah (2008-2009).

He initiated the IEEE RAS Technical Committee on Robotic Hands, Grasping, and Manipulation and served as its first co-Chair. Yu Sun also served on several editorial boards as an Associate Editor and Senior Editor, including IEEE Transactions on Robotics, IEEE Robotics and Automation Letters (RA-L), ICRA, and IROS.

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Bill Smart, Professor of Mechanical Engineering and Robotics at Oregon State University, helped start competitions as part of ICRA. In this episode, Bill dives into the high-level decisions involved with creating a meaningful competition. The conversation explores how competitions are there to showcase research, potential ideas for future competitions, the exciting phase of robotics we are currently in, and the intersection of robotics, ethics, and law.

Bill Smart

Dr. Smart does research in the areas of robotics and machine learning. In robotics, Smart is particularly interested in improving the interactions between people and robots; enabling robots to be self-sufficient for weeks and months at a time; and determining how they can be used as personal assistants for people with severe motor disabilities. In machine learning, Smart is interested in developing strategies for teaching robots to act effectively (or even optimally), based on long-term interactions with the world and given intermittent and at times incorrect feedback on their performance.

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At ICRA 2022, Competitions are a core part of the conference. We shine a spotlight on influential competitions in Robotics. In this episode, Dr Liam Paull talks about the Duckietown Competition, where robots drive around Rubber Ducky passengers in an autonomous driving track.

Dr. Liam Paull

Liam Paull is an assistant professor at l’Université de Montréal and the head of the Montreal Robotics and Embodied AI Lab (REAL). His lab focuses on robotics problems including building representations of the world (such as for simultaneous localization and mapping), modeling of uncertainty, and building better workflows to teach robotic agents new tasks (such as through simulation or demonstration). Previous to this, Liam was a research scientist at CSAIL MIT where he led the TRI funded autonomous car project. He was also a postdoc in the marine robotics lab at MIT where he worked on SLAM for underwater robots. He obtained his PhD from the University of New Brunswick in 2013 where he worked on robust and adaptive planning for underwater vehicles. He is a co-founder and director of the Duckietown Foundation, which is dedicated to making engaging robotics learning experiences accessible to everyone. The Duckietown class was originally taught at MIT but now the platform is used at numerous institutions worldwide.

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ApexAI is driving advances in ROS2 to make it viable for use in autonomous vehicles. The changes they are implementing are bridging the gap between the automotive world and robotics.

Joe Speed, VP of Product at ApexAI, dives into the current multi-year development process of bringing a car to market, and how ApexAI will transform this process into the shorter development time we see with modern technology. This technology was showcased at the Indy Autonomous Challenge where million-dollar autonomous cars raced each other on a track.

Apex.OS is a certified software framework and SDK for autonomous systems that enable software developers to write safe and certified autonomous driving applications compatible with ROS 2.

Joe Speed

Joe Speed is VP of Product & Chief Evangelist at Apex.AI. Prior to joining Apex.AI, Joe was a member of Open Robotics ROS 2 TSC, Autoware Foundation TSC, Eclipse OpenADx SC, and ADLINK Technology’s Field CTO driving robotics and autonomy.

Joe has spent his career developing and advocating open-source at organizations including Linux Foundation and IBM where he launched IBM IoT and co-founded the IBM AutoLAB automotive incubator. Joe helped make MQTT, IoT protocol, open-source and convinced the automakers to adopt it.

Joe is working to do the same for Apex.AI’s safe ROS 2 distribution and ROS middleware. Joe has developed a dozen advanced technology vehicles but is most proud of helping develop an accessible autonomous bus for older adults and people with disabilities.

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Machine Learning at the edge is gaining steam. BrainChip is accelerating this with their Akida architecture, which is mimicking the human brain by incorporating the 5 human senses on a machine learning-enabled chip.

Their chips will let roboticists and IoT developers run ML on device for low latency, low power, and low-cost machine learning-enabled products. This opens up a new product category where everyday devices can affordably become smart devices.

Rob Telson

Rob is an AI thought-leader and Vice President of Worldwide Sales at BrainChip, a global tech company that has developed artificial intelligence that learns like a brain, whilst prioritizing efficiency, ultra-low power consumption, and continuous learning. Rob has over 20 years of sales expertise in licensing intellectual property and selling EDA technology and attended Harvard Business School.

Links

• BrainChip Inc
• Akida Neural Processor SOC
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Over the past decade, camera technology has made gradual, and significant improvements thanks to the mobile phone industry. This has accelerated multiple industries, including Robotics. Today, Davide Scaramuzza discusses a step-change in camera innovation that has the potential to dramatically accelerate vision-based robotics applications.

Davide Scaramuzza deep dives on Event Cameras, which operate fundamentally different from traditional cameras. Instead of sampling every pixel on an imaging sensor at a fixed frequency, the “pixels” on an event camera all operate independently, and each responds to changes in illumination. This technology unlocks a multitude of benefits, including extremely highspeed imaging, removal of the concept of “framerate”, removal of data corruption due to having the sun in the sensor, reduced data throughput, and low power consumption. Tune in for more.

Davide Scaramuzza

Davide Scaramuzza is a Professor of Robotics and Perception at both departments of Informatics (University of Zurich) and Neuroinformatics (joint between the University of Zurich and ETH Zurich), where he directs the Robotics and Perception Group. His research lies at the intersection of robotics, computer vision, and machine learning, using standard cameras and event cameras, and aims to enable autonomous, agile, navigation of micro drones in search-and-rescue applications.

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Torrey Smith, Co-Founder of Endiatx, is changing the reputation endoscopies have for being uncomfortable. At Endiatx, they are developing a pill-sized robot that you swallow, which will then livestream your digestive system for a doctor to view. Our interviewer Abate dives in.

Torrey Smith
Torrey Smith is the Co-Founder & CEO of Endiatx, a medical robotics company that manufactures tiny robotic pills capable of active movement inside the human stomach with control over internet protocol. Prior to launching Endiatx, he developed medical devices in the areas of endometrial ablation, atherectomy, therapeutic hypothermia, sleep apnea, and vascular closure.

An aerospace engineer by training, he takes a keen interest in the deep tech sector and is a proud mentor of up-and-coming founders at the Founder Institute. He is also the principal founder of the international arts collective known as Sextant, and he has had his art featured in the Smithsonian.

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Amit Goel, Director of Product Management for Autonomous Machines at NVIDIA, discusses the new collaboration between Open Robotics and NVIDIA. The collaboration will improve the way ROS and NVIDIA’s line of products such as Isaac SIM and the Jetson line of embedded boards operate together.

NVIDIA’s Isaac SIM lets developers build robust and scalable simulations. Dramatically reducing the costs of capturing real-world data and speeding up development time.

Their Jetson line of embedded boards is core to many robotics architectures, leveraging hardware-optimized chips for machine learning, computer vision, video processing, and more.

The improvements to ROS will allow robotics companies to better utilize the available computational power, while still developing on the robotics-centric platform familiar to many.

For the developers out there, check out the ISAAC ROS Github repository to implement this yourself.

Amit Goel

Amit Goel is Director of Product Management for Autonomous Machines at NVIDIA, where he leads the product development of NVIDIA Jetson, the most advanced platform for AI computing at the edge.

Amit has more than 15 years of experience in the technology industry working in both software and hardware design roles. Prior to joining NVIDIA in 2011, he worked as a senior software engineer at Synopsys, where he developed algorithms for statistical performance modeling of digital designs.

Amit holds a Bachelor of Engineering in electronics and communication from Delhi College of Engineering, a Master of Science in electrical engineering from Arizona State University, and an MBA from the University of California at Berkeley.

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Anthony DiMare and Charles Chiau deep dive into how Bedrock Ocean is innovating in the world of Marine Surveys. At Bedrock Ocean, they are developing an Autonomous Underwater Vehicle (AUV) that is able to map the seafloor autonomously and at a high resolution. They are also developing a data platform to access, process, and visualize data captured from other companies at the seafloor.

Bedrock Ocean is solving two problems in the industry of Marine Surveying.
1. The vast majority of the seafloor is completely unmapped
2. The data that is captured from the seafloor is not standardized or centralized.

Seafloor data conducted by two different companies with the same or different hardware to capture the data can vary significantly in the calculated seafloor profile

Anthony DiMare
Anthony previously founded Nautilus Labs, a leading maritime technology company advancing the efficiency of ocean commerce through artificial intelligence. While at Nautilus, Anthony helped global companies solve challenges with distributed, siloed maritime data systems and built the early team that launched Nautilus Platform into large publicly listed shipping companies.

Charles Chiau
Charles, Bedrock’s CTO, was previously at SpaceX where he helped design the avionics systems for Crew Dragon. He also was a system integration engineer at Reliable Robotics working on their autonomous aviation system and was the CTO of DeepFlight where he worked on manned submersibles including ones for Tom Perkins, Richard Branson, and Steve Fossett.

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Abate talks to Maxime Gariel, CTO of Xwing about the autonomous flight technology they are developing.

At Xwing, they are converting traditional aircraft into remotely operated aircraft. They do this by retrofitting planes with multiple sensors including cameras, radar, and lidar, and by developing sensor fusion algorithms to allow their planes to understand the world around them, using highly accurate perception algorithms.

Xwing’s autonomous flight technology allows a plane to taxi in the airport, takeoff, fly to a destination, avoid airborne and ground threats, and land, all without any human input. This technology not only enables autonomous flight but may also enhance the safety of manned aircraft by improving a plane’s ability to understand its surroundings.

Maxime Gariel

Maxime Gariel is the CTO of Xwing, a San Francisco based startup whose mission is to dramatically increase human mobility using fully autonomous aerial vehicles. Xwing is developing a Detect-And-Avoid system for unmanned and remotely piloted vehicles. Maxime is a pilot but he is passionate about making airplanes fly themselves.

Maxime joined Xwing from Rockwell Collins where he was a Principal GNC Engineer. He worked on autonomous aircraft projects including DARPA Gremlins and the AgustaWestland SW4 Solo autonomous helicopter. Before becoming Chief Engineer of the SW4 Solo’s flight control system, he was in charge of the system architecture, redundancy, and safety for the project.

Before Rockwell Collins, he worked on ADS-B based conflict detection as a postdoc at MIT and on autoland systems for airliners at Thales. Maxime earned his MS and PhD in Aerospace Engineering from Georgia Tech and his BS from ISAE-Supaéro (France).

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Intel RealSense ID was designed with privacy as a top priority. Purpose-built for user protection, Intel RealSense ID processes all facial images locally and encrypts all user data. (Credit: Intel Corporation)

Intel RealSense is known in the robotics community for its plug-and-play stereo cameras. These cameras make gathering 3D depth data a seamless process, with easy integrations into ROS to simplify the software development for your robots. From the RealSense team, Joel Hagberg talks about how they built this product, which allows roboticists to perform computer vision and machine learning at the edge.

Joel Hagberg

Joel Hagberg leads the Intel® RealSense Marketing. Product Management and Customer Support teams. He joined Intel in 2018 after a few years as an Executive Advisor working with startups in the IoT, AI, Flash Array, and SaaS markets. Before his Executive Advisor role, Joel spent two years as Vice President of Product Line Management at Seagate Technology with responsibility for their $13B product portfolio. He joined Seagate from Toshiba, where Joel spent 4 years as Vice President of Marketing and Product Management for Toshiba’s HDD and SSD product lines. Joel joined Toshiba with Fujitsu’s Storage Business acquisition, where Joel spent 12 years as Vice President of Marketing, Product Management, and Business Development. Joel’s Business Development efforts at Fujitsu focused on building emerging market business units in Security, Biometric Sensors, H.264 HD Video Encoders, 10GbE chips, and Digital Signage. Joel earned his bachelor’s degree in Electrical Engineering and Math from the University of Maryland. Joel also graduated from Fujitsu’s Global Knowledge Institute Executive MBA leadership program.

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Abate interviews Tessa Lau on her startup Dusty Robotics which is innovating in the field of construction.

At Dusty Robotics, they developed a robot to automate the laying of floor plans on the floors in construction sites. Typically, this is done manually using a tape measure and reading printed out plans. This difficult task can often take a team of two a week to complete. Time-consuming tasks like this are incredibly expensive on a construction site where multiple different teams are waiting on this task to complete. Any errors in this process are even more time-consuming to fix. By using a robot to automatically convert 3d models of building plans into markings on the floors, the amount of time and errors are dramatically reduced.

Dr. Tessa Lau

Dr. Tessa Lau is an experienced entrepreneur with expertise in AI, machine learning, and robotics. She is currently Founder/CEO at Dusty Robotics, a construction robotics company building robot-powered tools for the modern construction workforce. Prior to Dusty, she was CTO/co-founder at Savioke, where she orchestrated the deployment of 75+ delivery robots into hotels and high-rises. Previously, Dr. Lau was a Research Scientist at Willow Garage, where she developed simple interfaces for personal robots. She also spent 11 years at IBM Research working in business process automation and knowledge capture. More generally, Dr. Lau is interested in technology that gives people super-powers, and building businesses that bring that technology into people’s lives. Dr. Lau was recognized as one of the Top 5 Innovative Women to Watch in Robotics by Inc. in 2018 and one of Fast Company’s Most Creative People in 2015. Dr. Lau holds a PhD in Computer Science from the University of Washington.

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In this episode, Abate follows up with Benjamin Pietro Filardo, founder of Pliant Energy Systems and NACROM, the North American Consortium for Responsible Ocean Mining. Pietro talks about the deep sea mining industry, an untapped market with a massive potential for growth. Pietro discusses the current proposed solutions for deep sea mining which are environmentally destructive, and he offers an alternative solution using swarm robots which could mine the depths of the ocean while creating minimal disturbance to this mysterious habitat.

Benjamin “Pietro” Filardo
After several years in the architectural profession, Pietro founded Pliant Energy Systems to explore renewable energy concepts he first pondered while earning his first degree in marine biology and oceanography. With funding from four federal agencies he has broadened the application of these concepts into marine propulsion and a highly novel robotics platform.

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Abate interviews Benjamin “Pietro” Filardo, CEO and founder of Pliant Energy Systems. At PES, they developed a novel form of actuation using two undulating fins on a robot. These fins present multiple benefits over traditional propeller systems including excellent energy efficiency, low water turbulence, and an ability to maneuver in water, land, and ice. Aside from its benefits on a robot, Pietro also talks about its advantages for harnessing energy from moving water.

Watch this episode on YouTube

Benjamin “Pietro” Filardo
After several years in the architectural profession, Pietro founded Pliant Energy Systems to explore renewable energy concepts he first pondered while earning his first degree in marine biology and oceanography. With funding from four federal agencies he has broadened the application of these concepts into marine propulsion and a highly novel robotics platform.

Links

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In this episode, Abate interviews Josh Lessing, co-founder and CEO of Root AI. At Root AI they are developing a system that tracks data on the farm and autonomously harvests crops using delicate grippers and computer vision. Lessing talks about the path they took to build a product with good market fit and how they brought a venture capital backed startup to market.

Josh Lessing

Josh is one of the world’s leading minds on developing robotics and AI systems for the food industry, previously serving as the Director of R&D at Soft Robotics Inc. His current venture, Root AI, is integrating advanced robotics, vision systems and machine perception to automate agriculture. Josh was a Postdoctoral Fellow in Materials Science & Robotics at Harvard University, having earned his Ph.D. studying Biophysics & Physical Chemistry at the Massachusetts Institute of Technology and received an Sc.B. in Chemistry from Brown University.

Links

• Download mp3 (16.0 MB)
• Listen to this episode on Spotify
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In this episode, Abate interviews Erin Bishop from Sense Photonics about the technology in their “Solid State” LiDAR sensors that allows them to detect objects more accurately and over a larger field of view than traditional scanning LiDAR. Erin dives into the technical details of Solid State Lidar, discusses the applications and industries of the technology.

Erin Bishop

Erin operates at the intersection of product management, project engineering, customer development, and product-market fit within 3D camera company Sense Photonics. Over the past several years, she has worked to initiate the market appetite for telepresence robots, indoor mobile robots, and robotic picking in warehouses. Erin has made appearances at CES, UX Week, RoboBusiness, HardwareCon, and Mobile Future Forward.

Erin creates partnerships that result in large-scale deployment of the company’s revolutionary solid-state FLASH architecture. Prior to joining Sense, she held product manager, marketing, and research roles with several robotics companies, including Industrial Perception, Inc. – which was acquired by Google in 2013 – Adept Technology, iRobot, and DEKA. Erin earned her Master’s in Mechanical Engineering from UMass Lowell, where she performed extensive research in Human-Robot Interaction.

Links

• Video of Solid State Lidar in action
• Download mp3 (23.2 MB)
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In this episode, Abate interviews David Mindell, co-founder of Humatics. David discusses a system they developed that can detect the location of a special tracking device down to a centimeter level accuracy. They are currently developing a device to detect location down to a millimeter level accuracy. This solves a the core problem of localization for robots. David discusses the technology behind these products and their applications.

David Mindell

David co-founded Humatics with a mission to revolutionize how people and machines locate, navigate and collaborate. He is a professor of Aeronautics and Astronautics at MIT, as well as the Dibner Professor of the History of Engineering and Manufacturing, and Chair of the MIT Task Force on the Work of the Future. He has participated in more than 25 oceanographic expeditions and is an inventor on 8 patents in autonomous helicopters, AI-assisted piloting, and RF navigation. He is the author of five books, including Our Robots, Ourselves:Robotics and the Myths of Autonomy (2015) and Digital Apollo: Human and Machine in Spaceflight (2008). David has undergraduate degrees from Yale and a Ph.D. from MIT.

Links

• Download mp3 (15.9 MB)
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In this episode Abate interviews Yariv Bash from Flytrex. Yariv discusses how Flytrex works in cooperation with local businesses in a city to use drones to rapidly transport goods in a local region. A practical application is the delivery of food from local restaurants. Yariv discusses Flytrex’s plans for using their USD$7.5 million series B round of funding.

Yariv Bash

Yariv Bash is the CEO of Flytrex, a drone technology company providing comprehensive, autonomous drone delivery systems that enable any business, from SMBs to e-commerce giants, to integrate instant, autonomous on-demand drone delivery into their offering. Flytrex is an end-to-end drone logistics service. Prior to Flytrex, Yariv was the Founder and CEO of SpaceIL – the Israeli team at the Google Lunar X-Prize competition. SpaceIL’s mission: landing an unmanned spaceship on the moon, for a fraction of the traditional cost. Under Yariv’s management, the non-profit SpaceIL raised more than $30 million.

Links

• Download mp3 (13.1 MB)
• Flytrex
• Flytrex 2018 Recap
• Flytrex Launches Drone Delivery in Reykjavík, Iceland
• Subscribe to Robots using iTunes
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• Support us on Patreon

In this episode, Abate interviews Andrew Stein from Anki. At Anki they developed an engaging robot called Cozmo which packs sophisticated robotic software inside a lifelike, palm sized, robot. Cozmo recognizes people and objects around him and plays games with them. Cozmo is unique in that a large amount of development has been implemented to make his animations and behavior feel natural, in addition to focusing on classical robotic elements such as computer vision and object manipulation.

Andrew Neil Stein

Andrew Stein is the Head of Robotics & AI at Anki, where he began working on the Cozmo project more than four years ago as the team’s first member. He has contributed to several core systems of the product, including vision, cube manipulation, animation streaming, localization, high-level behaviors, and low-level actions. He earned his Ph.D. from the Robotics Institute at Carnegie Mellon University, and his Bachelor’s and Master’s degrees in Electrical and Computer Engineering from the Georgia Institute of Technology.

Links

• Anki’s Website
• Cozmo’s Website
• Andrew Stein LinkedIn
• Download mp3 (19.1 MB)
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In this episode, Abate interviews Peter Harris from HighRes Biosolutions about automation in the field of drug discovery. At HighRes Biosolutions they are developing modular robotic systems that work alongside scientists to automate laboratory tasks. Because the requirements of each biomedical research laboratory are so varied, the robotic systems are specifically tailored to meet the requirements of each lab.

Peter Harris
Peter Harris is the CEO of HighRes Biosolutions. Prior to HighRes, Peter was VP and Managing Director at Axel Johnson, Inc. He spent most his career as the President & CEO of Cadence, Inc., a high technology medical device manufacturing and engineering firm enabling medical companies to bring better devices to market faster. Peter has been a Visiting Executive Lecturer at the Darden School of Business at the University of Virginia for over 10 years.

Links

• HighRes Biosolutions Website
• “Collaborative robots are redefining lab automation” by Peter Harris
• Peter Harris LinkedIn
• Download mp3 (16.6 MB)
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In this episode Abate talks with Zhe Zhang from Perceptin where they are building embedded platforms for robots to do Simultaneous Localization and Mapping (SLAM) algorithms in real time. Zhe explains the methods they incorporate such as sensor fusion and hardware synchronization to make a highly accurate SLAM platform for IOT, consumer, and automotive grade robots.

Zhe Zhang

Zhe is the co-founder and CEO of PerceptIn. Prior to founding PerceptIn, he worked at Magic Leap in Silicon Valley and prior to that he worked for five years at Microsoft. Zhang has a PhD in Robotics from the State University of New York and an undergraduate degree from Tsinghua University.

Further research recommended by Zhe:

1. Probabilistic Data Association for Semantic SLAM
2. A Comparative Analysis of Tightly-coupled Monocular, Binocular, and Stereo VINS
3. Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

Links

• Perceptin Website
• Zhe’s personal website
• Download mp3 (17.0 MB)
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In this episode, Abate De Mey speaks with Rick Faulk, CEO of Locus Robotics, about warehouse automation with collaborative robots. At Locus Robotics, they increase the productivity of workers in e-commerce warehouses by using robot helpers to transport items that are passed to them by the workers. The lightweight autonomous robots move at a similar pace to their co-workers, use LIDAR and computer vision to detect people and avoid collisions. This allows people to share the warehouse floor with the robots. The collaborative robotic system is lightweight and can be adapted to existing warehouses with minimal alterations.

Rick Faulk
Rick Faulk, CEO at Locus Robotics, leads the executive team and is responsible for overall strategy and execution. He has over thirty years of experience in executive management, sales and marketing for some of the world’s most successful technology companies such as Intronis, j2 Global, WebEx, Intranets.com, Lotus Development, Mzinga, and PictureTel. Rick also currently sits on various boards including INfluitive and Hostway, and is an advisor to a number of early-stage companies.

Links

• Locus Robotics
• Locus Robotics at PROMAT 2017
• Download mp3 (11.9 MB)
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In this episode, Abate De Mey interviews speakers from the Artificial Intelligence and Drone tracks at RoboUniverse San Diego and the Inside 3D Printing Conference. Dr. Douglas Stow from the Drones track discusses the use of drones in analyzing large scale changes over time in terrains, and its applications in determining the extent of damage after a natural disaster. From the Artificial Intelligence track, Dr. Eugene Izhikevich discusses how his company, Brain Corporation, converts manually driven industrial machines into autonomous robots. Wrapping it up, Cullen Hilkene from 3Diligent discusses how his company optimizes the work flow and communication layer between a diverse set of 3D print vendors and their customers.

Dr. Douglas Stow
Dr. Stow is a Professor of Geography at San Diego State University (SDSU) and has been on the faculty for 33 years. He has worked in the remote sensing field for almost 40 years and his research focuses on multitemporal image analysis. He is the primary instructor of remote sensing courses at SDSU and is the Co-Director of the Center for Earth Systems Analysis Research.

Dr. Eugene Izhikevich
Known for his contributions to the theory of spiking networks, in 2005 Dr. Izhikevich implemented the world’s largest thalamo-cortical model. It simulated one hundred billion neurons and one quadrillion synapses – the same number as the human brain. In addition, Dr. Izhikevich was a senior fellow in Theoretical Neurobiology at the San Diego based Neurosciences Institute and is the founder of Scholarpedia, a free, peer-reviewed encyclopedia.

Cullen Hilkene
Cullen Hilkene is CEO of 3Diligent, “the 3D Printing Partner for Every Business.” 3Diligent is a web-based rapid manufacturing service described as “Uber meets Amazon for 3D Printing, CNC Machining, and Molding and Casting.” Hilkene was introduced to 3D printing while working on a project with Deloitte Consulting, where he was exposed to many of the business challenges that it began solving. Cullen received his MBA from the UCLA Anderson School of Management and his BA from Princeton University.

Links

• Download MP3 (20.3 Mb)
• Research into remote sensing optimization (Dr. Stow)
• Autonomous driving using BrainCorp technology
• Overview of 3Diligent platform

In this episode, Abate De Mey interviews two speakers from the Agricultural track of the RoboUniverse 2016 conference in San Diego: Dan Harburg of Soft Robotics Inc. and Matthew Borzage of BioTac. Borzage and Harburg discuss their distinct approaches to advancing gripping technology in Agriculture. Borzage stresses the importance of tactile sensing while Harburg pushes for low cost, soft grippers with no on-board sensors.

Dan Harburg
Dan Harburg is the Director of Business Development at Soft Robotics Inc., a venture-backed startup company developing a soft end-of-arm tool for the food industry. Harburg was previously an entrepreneur-in-residence at North Bridge Venture Partners, working with scientists at Harvard, MIT, and the University of Illinois to commercialize technologies at the intersection of materials, medicine, and advanced manufacturing.

Matthew Borzage
Matthew Borzage is a co-founder and the Head of Business Development for SynTouch. Borzage has a background in biomedical and medical device engineering and sales. As Head of Business Development, Matt is interested in how tactile sensing can be used in robotics and prosthesis. 

Links

• Soft Robotics Inc.
• Bakery Automation with a Soft Touch
• SynTouch Inc.
• Biomimetic Design of BioTac
• Download Episode (16.2 MB)

In this episode, Abate De Mey interviews Edward Neff, founder of SMAC Corporation. Mr. Neff discusses how breakthroughs in his company have allowed them to develop linear actuators compact enough to be used to actuate robotic fingers. Companies like Apple and Samsung push for the development of robotic fingers to perform lifelike tests on their phones.

The robotic fingers developed using SMAC’s linear servo motors simulate a button press on a screen by employing a strong downwards force, as well as small sideways forces tangential to the screen. This mimics a realistic way that people interact with their touchscreens. The robotic fingers can also gently bump into surfaces, pull, and push objects. Other applications for these fingers include electronic circuit board assembly, and screwing bottle caps onto bottles.

Edward Neff

Edward Neff grew up in Birmingham, Michigan (USA) and is a graduate of the University of Michigan. He has a career history of managing automation companies in Belgium and Japan. Mr. Neff is the founder of SMAC Corporation in Carlsbad, California. SMAC is a leading manufacturer of precision programmable electric actuators. The company is planning a 2018 IPO. Mr Neff has over 40 patents in robotics and serves as a Director in BioTech and Medical Device companies.

Links

• SMAC Website
• Robotic Finger using a SmartPhone (YouTube)
• Robotic Finger at the Tokyo Robot Show (YouTube)
• Robotic Finger VS Human (YouTube)
• Smart Assembly Video (YouTube)
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In this episode, Abate De Mey interviews Jeff Sprenger, founder of the startup Xemory in Vermont, USA. At Xemory they are developing a robot simulation game called Xemo, where players learn to animate virtual robots.

Complex motion at each joint is broken down into its more fundamental components, called degrees of freedom. Players are faced with the challenge of controlling the several degrees of freedom to recreate lifelike motions such as crawling, walking, jumping, and even dancing. These challenges are similar to the ones faced by roboticists trying to develop lifelike, robust and balanced motions for legged robots.

Sprenger discusses the unique ways different age groups and genders interact with the software. Through incorporating feedback from the students, Sprenger adds new activities and challenges to keep students engaged and challenged, improving their understanding of robot control.

Jeff Sprenger
Jeff Sprenger is the founder and CEO of Xemory Software, creator of the Xemo Robot Simulation game. Jeff founded Xemory in 2014 when he received an NSF grant to develop interactive robotics software for STEM education. The company received a second, follow up grant in 2015 to further develop and bring Xemo to market. Jeff has worked in scientific and business software for over 30 years, with eleven years as VP of Technology at MBF Bioscience. Jeff is also the co-organizer for Vermont’s monthly game developer meetup.

Links:

• Collaborator Dr Josh Bongard from The Morpholology, Evolution and Cognition lab advises the Xemo team and heads the MECL robotics lab at the University of Vermont
• Email robots@xemory.com for questions or suggestions about Xemo
• Download mp3 (11.1 MB)
• Subscribe to Robots using iTunes
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Transcript below.

In this episode, Abate De Mey interviews Guido De Croon about Evolutionary Robotics and its use to design behaviors for flying robots. They discuss a recent paper by Kirk Schepe et al., in which the DelFly UAV robot learns to fly through an open window when trapped inside a room thanks to a controller optimized using a Genetic Algorithm. The controller is programmed using a Behavior Tree Framework, which is more intuitive and adaptable than the traditional Neural Network framework. This helps the user to manually adapt the controller to handle the differences between the simulation and the real world. They go on to discuss the challenges and benefits of using Evolutionary Robotics to learn robot behaviors.

Video of the Evolutionary Robotics strategy used to develop a controller for the DelFly robot:

Guido De Croon
Guido de Croon is Assistant Professor at the Micro Air Vehicle lab of Delft University of Technology in the Netherlands. His research interest lies with computationally efficient algorithms for robot autonomy, with a particular focus on computer vision and evolutionary robotics.

Links:

• Download mp3 (15.0 MB)
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• Guido de Croon’s Website

Transcript
This transcript has been edited for clarity.

Abate De Mey: Welcome to the Robots Podcast. Could you please introduce yourself to our listeners?

Guido De Croon: Hi, I’m Guido De Croon and I’m assistant professor in the Micro Air Vehicle Lab of TU Delft in the Netherlands. I work on the artificial intelligence of tiny robots.

Abate De Mey: You also do work with evolutionary robotics. Can you talk about evolutionary robotics, and what are some real world applications of them?

Guido De Croon: Evolutionary robotics is a domain in which we chase the following dream: we would like to have a general method that if we give a task for a robot, we only need to come up with the fitness function. A way of saying how good a robot is doing the task. Then, there’s an artificial evolutionary process that takes over the design work and evolves the robots, like the body, sensors, actuators and the controller do this task.

Abate De Mey: What are some things that you optimize using evolutionary robotics tactics?

Guido De Croon: Typically, it’s a task you want the robot to do. You come up with a fitness function, which is actually a difficult part of evolutionary robotics. In the last few years, we’ve also seen special fitness functions like rewarding robots to do novel stuff. We call this novelty search. In my case, I’ve been evolving robots to do different tasks, such as finding odor sources. Recently, I’ve been evolving small robots to perform the task of flying in a room, avoiding obstacles, finding a window and flying through the window.

Abate De Mey: What are some limitations of evolutionary robotics?

Guido De Croon: I don’t think there are fundamental limitations to evolutionary robotics because we will have technologies in the end to perform an actual evolution with it. I think the major limitation at the moment is that if you want to do evolution on real robots, it will happen in real time, which means it can actually take a very long time. The worst case it really takes as long as a real evolution. You can imagine that … Especially if we want a complex robot to do a complex task, then this might take a really long time.

That’s why most people in evolutionary robotics evolve robots in simulation. You have a simulation with your robot in it. It has a task to do. You can test out thousands of what we call individuals to different robot solutions. You can have the best ones procreate and create offspring. Then at the end of this artificial evolutionary process and simulation, you need to transfer the best solution to the real world. This is a difficult step because simulation goes fast also because you don’t model everything that’s happening in the real world. The real robot you use is typically different than the one that was used in simulation, so this is a challenge.

There’s difference between the simulation and reality in the field of evolutionary robotics. It’s called the reality gap. Crossing the reality gap is another major challenges at the moment.

The second limitation that’s there is the complexity of the task it outperforms is pretty limited. We have really quite studies on finding light or avoiding obstacles, things that can be done with simple controllers. The question is: how well the approach scales also to more complex problems? Finding other sources in turbulent conditions or really navigating in an environment, being able to find something and then return to the home location.

Abate De Mey: What are some tactics you use to overcome the reality gap?

Guido De Croon: There are different tactics that have been developed. One way is to do an evolution in simulation, then taking the solution and continuing the evolution on real robots. You perform a lot of the evolution in simulation, and then just perform final adaptations on the platform. Still, the real robot may be quite different. It may take generations before you get the right solution. This way of handling the reality gap still requires a long time.

There are other ways though. There are ways in which the mismatch between simulation and reality is modeled during evolution. What they do then is once every generation, for example, one in every hundred generations, they try out an individual on real robots. They take the difference in the performance. They try to model this in the simulation and try to adapt the simulation and predict if solutions will work in the real world. That’s another approach.

I think that the final approach, which may be very successful, is to combine evolutionary learning, which is a slow process, with developmental capabilities. They call this evo-devo. It means that you evolve the robot and its controller, but you also evolve adaptive mechanisms that will allow the robot to also learn online, so during operation.

Abate De Mey: Have you implemented evo-devo before in a project?

Guido De Croon: Yes, some while ago. Until now it’s generally acknowledged that this will be important, but the golden bullet there has not been discovered yet. I do think that in the future this is promising.

The question why not is perhaps a good one as well. Why didn’t we try that yet? Also, because the learning mechanism has to be really fast. In my case, I focus on robots such as small flying robots. Suppose you combine evolutionary learning with reinforcement learning, still the robot will have to do reinforcement learning. Also reinforcement learning itself can take quite some time. It involves risks for the robot. Then you don’t really solve your problem. You really need to find the right combinations of developmental algorithms and evolutionary algorithms.

Abate De Mey: Could you tell us about a project where you have implemented evolutionary robotics?

Guido De Croon: Yes. I’ve implemented the evolutionary robotics in quite a few projects. I think one of the ones that’s perhaps a very good example is recently we implemented evolutionary robotics for our tiny flying robot called the DelFly Explorer. This is a dragonfly-like robot. It weighs 20 grams. In those 20 grams, we have a stereo vision system so two cameras and a small processor with which the DelFly can see obstacles and can do obstacle avoidance.

Now, we developed this DelFly Explorer a few years ago. At the time, we showed that it could avoid obstacles, fly around completely by itself, without any human intervention or without any off-board computer. Our PhD student Sjoerd Tijmons, he made what I think is a very elegant algorithm to control the robot. The main idea behind the algorithm is that the DelFly always keeps a space in front of it, a free space, large enough to make a turnaround maneuver. If it sees an obstacle coming into the zone, it will do this prefixed maneuver. It always turn in front of the obstacle and then it can circle as long as it wants until it finds a new flight direction. This, I think, can be compared a bit with some insect behaviors that you see in nature. We were, of course, very happy with it.

Then we thought now we want to do another task so so we want, for example, our DelFly to fly through a window, to fly in a room, find a window and fly through it. Do we then have to search another Sjoerd, another PhD student again to come up with another very nice algorithm? The answer actually is no. It will be great if we can give this task to an evolutionary process and that this evolutionary process finds a solution that’s, not only effective and robust, but also as computationally as efficient as the solution we found ourselves for obstacle avoidance. This is very important, for example, for these small flying robots because the processor that we have onboard, for example, it has only a 192 kilobytes of memory.

The nice thing about evolutionary robotics is that you can beforehand define the structure that evolution has to evolve, and you can ensure that it’s computationally efficient enough. If it then finds a solution, then it fits within the constraints of the onboard platform. This project we did last year, it gave us really some insight into this matter and into how to cross the reality gap.

Abate De Mey: How successful was the DelFly, which was optimized with the evolutionary robotics framework, at flying through the window?

Guido De Croon: That’s, of course, the most important question. The approach we took is quite different from what is typically done in evolutionary robotics. In evolutionary robotics it’s, of course, bio-inspired, inspired by natural evolution. This also typically implies that neural networks are used as the robot’s controller because this is also bio-inspired. It’s parallel. Of course, neural networks are very capable, especially also the deep neural networks. The problem of neural networks in evolutionary robotics is that it’s pretty hard to understand what evolution came up with. I’m not saying it’s impossible, not at all.

For multiple tasks, I’ve evolved neural networks, for example, also for odor source finding. When you do that afterwards, you can study it. You have the neural networks. You can, for example, clamp neurons to a certain value, or you can influence the input that the robot has, and you can analyze what happens then to the actions. Actually you can understand what it’s doing, what the strategy is.

When I was doing a study on odor source localization, I had robots in simulation performing really well. I tried to understand the networks. Then I was like, “When can you say that you really understand this?” In order to test whether I understood the strategy, I re-implement the strategy that evolution found in the neural network, and I re-implemented it as a finite-state machine. Then I ran the finite-state machine, and it got a similar performance as the original robot.

Then I thought, “Hey, this finite-state machine, if you look at it, you understand immediately what the robot is doing.” Suppose that you have this type of controller and you put it on a real robot, if something is not working correctly, then you can probably understand what is going wrong and adapt it yourself. That’s why when we wanted to evolve a controller for the real DelFly, we took a similar kind of controller. Instead of a finite-state machine, we took a behavior tree.

Behavior tree is a controller that is used a lot in the gaming industry because it’s pretty intuitive. In comparison with finite-state machines, it does not have what we call a state explosion. When you add complexity to the controller, it doesn’t immediately lead to a huge explosion of the controller structure. With the behavior tree, you can solve complex problems and the controller will still look pretty compressed, so pretty small.

In our case, for the DelFly flying through the window, we evolve the behavior trees. Evolution came up with a very elegant solution that was smaller than the hand designed one that we made ourselves and that had a higher success rate in simulation. Then, of course, the question was if we have this behavior tree, can we now understand it? Will this help us if we go to the real world?

We took the controller from simulation and put it into the real DelFly. Of course, the first time we flew it or the first few times, it would just crash against the wall. That was not very nice. Since we could easily see in what part of the tree it was active, we could easily adapt some parameters in the behavior tree to really boost the performance and get it more similar to the simulation performance.

Your original question was how successful was it? Well, in simulation it would get through the window 90% of the time. After tuning it on the real robot, it only flew through 53% of the time or 55. Don’t test me about the exact number. That’s, of course, much, much lower and really not satisfactory. The reason was that the robot which was evolved in simulation would, let’s say, take a bit more risk. It would fly very close to the window border which in simulation didn’t matter. In the real world, we were testing in a pretty artificial environment so a small room that we built indoors with window in it. It turned out that even in this indoor space, there were drafts and these drafts were coming through the window, and they were pushing the DelFly away from the window basically. If you then have a risky strategy of going a bit close to the border and you’re pushed away by the draft, then you actually hit the window border a bit more often than in simulation.

It taught us, yes, these behavior trees, they’re easier to understand. You can then manually adapt them for the real robot, but it doesn’t solve the reality gap completely which, of course, would have been almost too easy, of course, if that would have been the case. The robot exploited the fact that there was no wind in the simulation environment. Currently, we added, of course, wind to the simulation environment, and we’re re-evolving and seeing how that goes. It’s not the full answer to this problem, but it is a very promising step.

Abate De Mey: How adaptable would the controller that you created with evolutionary robotics be to a room with different parameters, different dimensions?

Guido De Croon: That’s also a very good question. Let’s say, partly a different room would be no problem. For example, our DelFly uses stereo vision. If you change the texture in the room, it would be okay. If you would make the walls completely textureless, that could be a problem actually. Then you would need other visual inputs, for example.

In this specific article that we wrote on this study, we only use one size of room. I think it was square and then it would fly through the window. One of the pitfalls of evolutionary robotics is that the solution that you evolved in simulation will exploit everything it can from that environment. If you only evolve in a square room, then it’s possible that it will generalize rectangular rooms. It’s also possible that your solution will be less good. You have to vary the parameters in simulation that you think will vary in reality. That’s, by the way, another thing that we’re doing at the moment for this task.

I think that there’s hardly an answer to that because I think the real answer would be to have the robot also develop by itself in the real world afterwards. There’s still a few other strategies. For example, one of the problems we had in this first study was that in simulation, we had the robot just set a rudder command. No, sorry. An aileron command. That means that it would directly set the ailerons of the robot. If you do that in real life, then this never gives the same result because, for example … Ailerons are like little things behind the wings that make it turn around, that make it change heading.

These ailerons on the real robot are already a bit asymmetric, for example. In simulation, we didn’t have a very accurate model not at all of the DelFly. If you would give an input of plus one, it would turn, for example, with I don’t know, some degrees per second to the right. Minus one would be the exact opposite reaction. In reality, this is not the case. It will turn perhaps easier to the right than to the left. It can even depend on environmental conditions whether it’s really turning with, I don’t know, 10 degrees per second or not.

One of the things we did now as well is to abstract away from those very low level commands and to take a bit higher level. The robot now sets, for example, a turn rate. It has a lower level controller that can actually execute this turn rates. In that sense, you also narrow the reality gap. We’re searching through all these kinds of possibilities and see how far we will get with that.

Abate De Mey: What are the largest hurdles that evolutionary robotics has to overcome to have widespread usage?

Guido De Croon: I think one is this reality gap that I’ve been talking about. One of the main reasons to use evolutionary robotics, especially for, for example, small robots, is that with fairly limited resources, computational, sensory, you’re still able to perform complex tasks. The way in which they do this is typically pretty ingenious. For example, we were talking about this odor source localization or if you think about fruit flies in your kitchen, they always find the … Well, at least in my kitchen, they do. They always find the bananas and the apples, especially in the summer. They really invade my kitchen. They solve this problem of finding the banana mostly by using the scent.

When you look at roboticist, then some of the robotic solutions are pretty complex. What they do is, for example, in the robots, the robot in its mind, it’s going to simulate how odor spreads through turbulent air. When it smells some odor, it will run some probabilistic dynamical model to see what’s the highest probability in its environment are to contain the source of the odor. That’s one approach. It’s computationaly very complex.

If you look at fruit flies, then they actually just alternate between two behaviors. If they don’t smell any odor, they will do casting, which means that they fly into the wind. If they do then smell something, then they will start surging, which means that they move upwind. When they lose the odor again, they will start casting again. This, of course, a very rough description of their behavior. By alternating between these two behaviors, in the end, they find the fruit. They do this in a computationally efficient way.

Now, what we want with evolutionary robotics is for any given task, for any given robot to be able to find such very smart strategies that work for the particular robots. If you want evolutionary robotics to be widely used, it really needs to work on the real robots. You need to cross the reality gap and the challenge then is not only to cross it, but to cross it and still keep this ability for finding these elegant, sensory motor solutions. That’s one large hurdle.

I think the second hurdle, I think I also mentioned it, the complexity of the task. It’s, of course, nice to have a robot that goes around and avoid obstacles. How can we evolve robots to do all kinds of tasks? The robot, for example, has to fly around in a greenhouse, has to deposit some larvae on plants or something and at the same time look for ripe fruits, come back to its nest to recharge, collaborate with the other drones in the greenhouse in order not to fly in each other’s way and to effectively cover all the area in the greenhouse. How do you use an evolutionary algorithm to come at such a complex solution?

I think also there’s a huge challenge. At the moment, we’re looking at these behavior trees also for this reason because behavior trees, they allow for certain sub behaviors to be very easily reused as a sub behavior in another behavior tree. I don’t know if this, of course, will be a step towards such a solution. I think that’s a large hurdle as well because if you wanted to be widespread, it needs to work on the actual robot, and the robots need to do useful and sometimes complex tasks.

Abate De Mey: Are evolutionary algorithms a one size fits all strategy for creating robot behaviors?

Guido De Croon: Not yet but that’s the the dream, Abate. We’re trying to replace ourselves as roboticist. That’s the dream that you can really give us a task. We just put the same evolutionary algorithm at work. It will be great, of course, in the future if you can evolve both the body, and the sensors, and the actuators, and the brain to come up with the ideal solution for this task.

Abate De Mey: How can our listeners learn more about evolutionary robotics?

Guido De Croon: That’s a good question. There is a book that was written a while ago by Stefano Nolfi and Dario Floreano. They talked about the earlier work in this area. I think there’s really some exciting work going on at the moment in evolutionary robotics. The book is a good starting point. More recent work, I would say Google is your friend.

Abate De Mey: Well, thank you very much for coming in today and discussing evolutionary robotics with us, Guido.

Guido De Croon: You’re welcome. It was my pleasure.

Pibot, robotic kit for the Raspberry Pi. Source: PiBot.org

In this episode, Abate De Mey interviews Harry Gee, founder of the robotics startup Agilic. Harry discusses his company and the robots he designed using the Raspberry Pi and Pi zero. He then discusses the basics for creating a robotics start-up and provides advice for aspiring entrepreneurs.

Harry Gee
Harry Gee has formal training in architecture and carpentry, and is a self-taught programmer, web designer, and roboticist. As technology continues to grow, there is an increased demand for tech-related knowledge for the younger generation. To accommodate this demand, Harry Gee focuses on developing educational robotic kits and workshops that empower young people with vital tech and hands-on skills. He believes robotics is an amazing playground for creative solutions to social and global challenges.

Links:

• Download mp3 (14.5 MB)
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• Agilic’s Website

Tony Prescott, Professor of Cognitive Neuroscience and Director of the Sheffield Center of Robotics believes that the medical industry could be at the forefront of changing the public perception of robotics. Why?  Because this is an industry that already understands how paramount it is, when dealing with the elderly, disabled and ill, to provide a service that is helpful, friendly and nonthreatening.

Speaking this week at RE.WORK – an event that explores the challenges of commercializing robotics – Prescott recalled the fear people had in the 90s about the coming of the Internet. Similarly, he said, reporters often ask him: “When will robots take over the world?” The question has perhaps become a cliché among roboticists, but in his presentation (aptly titled “Robots that know themselves will make better carers”) Prescott suggested that much can be learned from neuroscience and psychology to improve the safety and effectiveness – and by extension the public perception – of human-robot interaction.

The aging population is increasing rapidly, and there are not enough caregivers to satisfy the growing demand. It is projected that by 2060, there will be one dependent person for every working person in the EU. Per visit, caretakers have limited time in which to interact the elderly people in their care, and time must be divided between aiding with personal hygiene tasks, ordering/preparing food, and one-on-one conversation time. As such, seniors must often make difficult decisions to prioritize the services they wish to receive from caregivers.

Prescott said that personal robots could aid seniors with time consuming household tasks such as making a pot of tea, reaching for the salt, opening a jar, or microwaving food – tasks that could be frustrating if you have to wait around for help. Personal hygiene tasks such as showering or using the toilet can create an embarrassing dynamic between the elderly and the caretaker, and so also make ideal tasks for robot care assistants.

Prescott points out that the role of a robot should be to assist the senior and the caregiver, not to take over.  “Robots can help caregivers, not by replacing them, (but by working with them).”  With the aid of personal robots, caretakers could spend more time engaging in one-on-one conversation with the elderly, providing valuable social interaction.

Paro therapeutic robot

And yet we cannot stop people from getting personally attached to the items they own, Prescott explains. People like to glue fur to their Roombas to make them feel more like pets, and robot companions like the Paro have been shown to induce relaxation, reduce stress, and stimulate communication between patients and caregivers. The important distinction to remember, suggested Prescott, is that the companionship people feel toward their robots is more in line with how they feel toward their pets, and the need for human companionship will always remain critical.

If we can learn to design robots that can work as part of a care team, public perception of robots is sure to change.