Here are some postcards from 2021 and wishing you all the best for 2022!
According to Crunchbase, 26 robotics startups were founded and funded in 2021. Many others were founded but not funded, or funded but not founded. :)
AION Prosthetics
Electronics, Manufacturing, Medical Device, Robotics
AION Prosthetics develops a prosthetic system designed to provide an adjustable, durable, and affordable future for amputees.
http://aionprosthetics.com/
Atorika
Augmented Reality, EdTech, Education, Edutainment, Leisure, Personal Development, Robotics, Subscription Service, Virtual Reality
Atorika offers edutainment that adapts to any child.
https://www.atorika.fr/
BOTINKIT
Big Data, Robotics
Robots
ContRoL
Autonomous Vehicles, Mechanical Engineering
ContRoL focuses on developing a range of vehicles for controlled release.
https://control-create.mcmaster.ca/
DIWÖ
Artificial Intelligence, Drones, Machine Learning, Robotics, Software
Diwö is accelerating the transition into safe AI using autonomous UAVs and Robotics
https://www.xn–diw-una.com/
Drone Express
Artificial Intelligence, Delivery Service, Drones
Drone Express is a full-service logistics company that uses airborne autonomous drones for local package delivery.
https://droneexpress.ai
Eco City
Artificial Intelligence, CleanTech, Machine Learning, Mobile Apps, Robotics, Service Industry, Smart Cities
Mobile App, Robotic, AI, Machine Learning, Deep Learning, E-service
Eight Knot
Artificial Intelligence, Navigation, Robotics
Eight Knot designs and develops autonomous navigation system for water mobility using robotics & AI.
https://8kt.jp/
Elexir
Automotive, Autonomous Vehicles, Cyber Security, Software
We redefine mobility with the truly digital car
https://www.elexir.eu/en/
General Systems
Construction, Industrial Automation, Robotics
Construction Tech, Robotics, B2B, Automated Building Masonry
https://www.generalsystems.tech
Helgen Technologies
Agriculture, Consulting, Farming, Mining, Oil and Gas, Robotics, Software, Software Engineering, Waste Management
Software and hardware services for industrial robotics
https://www.helgen.tech
Mach9 Robotics
Machine Learning, Robotics, Software
Mach9 Robotics builds integrated hardware and software to make utility infrastructure inspection more accurate at a lower cost.
https://www.mach9.io/
Meili Technologies
Automotive, Autonomous Vehicles, Software
Meili provides automatic, contactless, in-vehicle medical emergency detection and interface with EMS to protect riders and make roads safer.
https://www.meilitechnologies.com
Mowito
Artificial Intelligence, Industrial Automation, Intelligent Systems, Robotics, Software
Mowito provides software tools for mobile robots, to enable them to navigate intelligently in indoor facilities.
https://mowito.in/
Muncho
Food and Beverage, Food Delivery, Hardware, Robotics, Transportation
Pizza cooked en-route to your door & delivered in as little as 5 minutes. Currently piloting in Philadelphia.
https://www.muncho.com
Outlift AI
Artificial Intelligence, Health Care, Information Technology, Robotics
Outlift AI develops robotic process automation designed to assist and automate back-office healthcare work.
https://www.outliftai.com/
PhiGent Robotics
3D Technology, Artificial Intelligence, Autonomous Vehicles
PhiGent Robotics offers autonomous driving solutions.
Qiangua Technology
Automotive, Autonomous Vehicles
Qiangua Technology is a Chinese autonomous driving truck company.
Serve Robotics
Food Delivery, Logistics, Robotics
Serve Robotics connects people with what they need locally via robots that are designed to serve people.
http://www.serverobotics.com
Socian Technologies
Aerospace, Artificial Intelligence, Drones, Machine Learning, Software
We create safer societies using AI-enhanced UAV technologies.
https://socian.io/
Tergeo Technologies
Industrial Automation, Machinery Manufacturing, Robotics, Waste Management
Tergeo Technologies is a developer of robotic solutions to sanitation challenges.
https://www.tergeotech.com
Urban Machine
Building Material, Robotics
Salvaging the past to build the future- Stealth Startup
http://www.urbanmachine.build
Wiingy
E-Learning, Education, Robotics
Wiingy is a unique combination of multiple learning and skill development methods including 1:1 live classes, DIY robotics kits.
https://www.wiingy.com/
Xbotod Technologies Ltd
Artificial Intelligence, Cloud Computing, Electronics, Embedded Systems, Internet of Things, Robotics
Shaping the next generation of technology and cities with Artificial Intelligence & Internet of Things (AIoT)
http://www.xbotod.com
Zbeetle
Artificial Intelligence, Electronics, Robotics
Zbeetle is a robotics innovation company engaged in producing cleaning robots.
http://www.zbeetle.com
Ziknes
Machinery Manufacturing, Robotics
Ziknes develops printing technology on industrial manufacturing of metals.
https://www.ziknes.com/
The use of industrial robots in factories around the world is accelerating at a high rate: 126 robots per 10,000 employees is the new average of global robot density in the manufacturing industries – nearly double the number five years ago (2015: 66 units). This is according to the 2021 World Robot Report. By regions, […] (Click here to read more)
Leading robotics experts such as Juan Aparicio and Ken Goldberg, share what they’ll be keeping an eye on in 2022.
The post 2022 robotics predictions from industry experts appeared first on The Robot Report.
Firstly, what is Deep Tech as opposed to Tech or technology enabled? Sometimes Deep Tech is regarded as a science based startup, sometimes it is regarded as disruptive to the status quo, sometimes it is regarded just as slow and hard, capital intensive, with a long ROI horizon. Or as something that investors aren’t ready […] (Click here to read more)
Researchers teamed up to develop a machine-learning program that can be connected to a human brain and used to command a robot. The program adjusts the robot’s movements based on electrical signals from the brain. The hope is that with this invention, tetraplegic patients will be able to carry out more day-to-day activities on their own. (Click here to read more)
China’s new five-year plan for robotics and Toronto banning sidewalk robots topped our coverage in December 2021.
The post Top 10 robotics stories of December appeared first on The Robot Report.
Using autonomous vehicle guidelines, a team has developed a system to improve interactions between people and robots. The way people interact safely with robots is at the forefront of today’s research related to automation and manufacturing, explains a researcher. She is one of several researchers who are working to develop systems that allow humans and robots to interact safely and efficiently. (Click here to read more)
Computer scientists and archeologists are working together to solve this ancient puzzle. (Click here to read more)
What happens when you let kids design their own social robot from scratch. (Click here to read more)
Happy holidays everyone! Here are some more robot videos to get you into the holiday spirit. Have a last minute holiday robot video of your own that you’d like to share? Send your submissions to daniel.carrillozapata@robohub.org […] (Click here to read more)
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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In this episode, Audrow Nash interviews Brett Aldrich, author of SMACC and CEO of Robosoft AI. Robosoft AI develops and maintains SMACC and SMACC2, which are event-driven, behavior state machine libraries for ROS 1 and ROS 2, respectively. Brett explains SMACC, its origins, other strategies for robot control such as behavior trees, speaks about the challenges of developing software for industry users and hobbyists, and gives some advice for new roboticists.
Episode Links
Podcast info
Cathy Wu is the Gilbert W. Winslow Assistant Professor of Civil and Environmental Engineering and a member of the MIT Institute for Data, Systems, and Society.
By Kim Martineau | MIT Schwarzman College of Computing
Cathy Wu is the Gilbert W. Winslow Assistant Professor of Civil and Environmental Engineering and a member of the MIT Institute for Data, Systems, and Society. As an undergraduate, Wu won MIT’s toughest robotics competition, and as a graduate student took the University of California at Berkeley’s first-ever course on deep reinforcement learning. Now back at MIT, she’s working to improve the flow of robots in Amazon warehouses under the Science Hub, a new collaboration between the tech giant and the MIT Schwarzman College of Computing. Outside of the lab and classroom, Wu can be found running, drawing, pouring lattes at home, and watching YouTube videos on math and infrastructure via 3Blue1Brown and Practical Engineering. She recently took a break from all of that to talk about her work.
Q: What put you on the path to robotics and self-driving cars?
A: My parents always wanted a doctor in the family. However, I’m bad at following instructions and became the wrong kind of doctor! Inspired by my physics and computer science classes in high school, I decided to study engineering. I wanted to help as many people as a medical doctor could.
At MIT, I looked for applications in energy, education, and agriculture, but the self-driving car was the first to grab me. It has yet to let go! Ninety-four percent of serious car crashes are caused by human error and could potentially be prevented by self-driving cars. Autonomous vehicles could also ease traffic congestion, save energy, and improve mobility.
I first learned about self-driving cars from Seth Teller during his guest lecture for the course Mobile Autonomous Systems Lab (MASLAB), in which MIT undergraduates compete to build the best full-functioning robot from scratch. Our ball-fetching bot, Putzputz, won first place. From there, I took more classes in machine learning, computer vision, and transportation, and joined Teller’s lab. I also competed in several mobility-related hackathons, including one sponsored by Hubway, now known as Blue Bike.
Q: You’ve explored ways to help humans and autonomous vehicles interact more smoothly. What makes this problem so hard?
A: Both systems are highly complex, and our classical modeling tools are woefully insufficient. Integrating autonomous vehicles into our existing mobility systems is a huge undertaking. For example, we don’t know whether autonomous vehicles will cut energy use by 40 percent, or double it. We need more powerful tools to cut through the uncertainty. My PhD thesis at Berkeley tried to do this. I developed scalable optimization methods in the areas of robot control, state estimation, and system design. These methods could help decision-makers anticipate future scenarios and design better systems to accommodate both humans and robots.
Q: How is deep reinforcement learning, combining deep and reinforcement learning algorithms, changing robotics?
A: I took John Schulman and Pieter Abbeel’s reinforcement learning class at Berkeley in 2015 shortly after Deepmind published their breakthrough paper in Nature. They had trained an agent via deep learning and reinforcement learning to play “Space Invaders” and a suite of Atari games at superhuman levels. That created quite some buzz. A year later, I started to incorporate reinforcement learning into problems involving mixed traffic systems, in which only some cars are automated. I realized that classical control techniques couldn’t handle the complex nonlinear control problems I was formulating.
Deep RL is now mainstream but it’s by no means pervasive in robotics, which still relies heavily on classical model-based control and planning methods. Deep learning continues to be important for processing raw sensor data like camera images and radio waves, and reinforcement learning is gradually being incorporated. I see traffic systems as gigantic multi-robot systems. I’m excited for an upcoming collaboration with Utah’s Department of Transportation to apply reinforcement learning to coordinate cars with traffic signals, reducing congestion and thus carbon emissions.
Q: You’ve talked about the MIT course, 6.007 (Signals and Systems), and its impact on you. What about it spoke to you?
A: The mindset. That problems that look messy can be analyzed with common, and sometimes simple, tools. Signals are transformed by systems in various ways, but what do these abstract terms mean, anyway? A mechanical system can take a signal like gears turning at some speed and transform it into a lever turning at another speed. A digital system can take binary digits and turn them into other binary digits or a string of letters or an image. Financial systems can take news and transform it via millions of trading decisions into stock prices. People take in signals every day through advertisements, job offers, gossip, and so on, and translate them into actions that in turn influence society and other people. This humble class on signals and systems linked mechanical, digital, and societal systems and showed me how foundational tools can cut through the noise.
Q: In your project with Amazon you’re training warehouse robots to pick up, sort, and deliver goods. What are the technical challenges?
A: This project involves assigning robots to a given task and routing them there. [Professor] Cynthia Barnhart’s team is focused on task assignment, and mine, on path planning. Both problems are considered combinatorial optimization problems because the solution involves a combination of choices. As the number of tasks and robots increases, the number of possible solutions grows exponentially. It’s called the curse of dimensionality. Both problems are what we call NP Hard; there may not be an efficient algorithm to solve them. Our goal is to devise a shortcut.
Routing a single robot for a single task isn’t difficult. It’s like using Google Maps to find the shortest path home. It can be solved efficiently with several algorithms, including Dijkstra’s. But warehouses resemble small cities with hundreds of robots. When traffic jams occur, customers can’t get their packages as quickly. Our goal is to develop algorithms that find the most efficient paths for all of the robots.
Q: Are there other applications?
A: Yes. The algorithms we test in Amazon warehouses might one day help to ease congestion in real cities. Other potential applications include controlling planes on runways, swarms of drones in the air, and even characters in video games. These algorithms could also be used for other robotic planning tasks like scheduling and routing.
Q: AI is evolving rapidly. Where do you hope to see the big breakthroughs coming?
A: I’d like to see deep learning and deep RL used to solve societal problems involving mobility, infrastructure, social media, health care, and education. Deep RL now has a toehold in robotics and industrial applications like chip design, but we still need to be careful in applying it to systems with humans in the loop. Ultimately, we want to design systems for people. Currently, we simply don’t have the right tools.
Q: What worries you most about AI taking on more and more specialized tasks?
A: AI has the potential for tremendous good, but it could also help to accelerate the widening gap between the haves and the have-nots. Our political and regulatory systems could help to integrate AI into society and minimize job losses and income inequality, but I worry that they’re not equipped yet to handle the firehose of AI.
Q: What’s the last great book you read?
A: “How to Avoid a Climate Disaster,” by Bill Gates. I absolutely loved the way that Gates was able to take an overwhelmingly complex topic and distill it down into words that everyone can understand. His optimism inspires me to keep pushing on applications of AI and robotics to help avoid a climate disaster.