Chatbots like ChatGPT and Claude have experienced a meteoric rise in usage over the past three years because they can help you with a wide range of tasks. Whether you're writing Shakespearean sonnets, debugging code, or need an answer to an obscure trivia question, artificial intelligence systems seem to have you covered. Those data aren't enough to teach a robot to be a helpful household or factory assistant, though. To understand how to handle, stack, and place various arrangements of objects across diverse environments, robots need demonstrations. You can think of robot training data as a collection of how-to videos that walk the systems through each motion of a task.

Atlas, the humanoid robot famous for its parkour and dance routines, has recently begun demonstrating something altogether more subtle but also a lot more significant: It has learned to both walk and grab things using a single artificial intelligence model. What is more, the robot's single learning model is showing some tantalizingly "emergent" skills, like the ability to instinctively recover when it drops an item without having been trained to do so. Boston Dynamics, the company that makes Atlas, together with the Toyota Research Institute (TRI), developed a generalist model that learns to control both arms and legs from a range of example actions. This is different from the norm: robots equipped with the ability to learn would usually rely on one model to walk and jump and another to grasp items. "The feet are just like additional hands, in some sense, to the model," says Russ Tedrake, a roboticist at the Toyota Research Institute and the Massachusetts Institute of Technology, who led the current work.

BACKGROUND David A. Shamma is a distinguished industry scientist researching people, AI, and HCI. As a curious child armed with a screwdriver, I disassembled things. I was interested in seeing how devices clicked together. On more than one occasion, a random spring would jettison out of the device and get me in trouble. Other times, I hoped my experiments went unnoticed.

As someone who quite enjoys the Zen of tidying up, I was only too happy to grab a dustpan and brush and sweep up some beans spilled on a tabletop while visiting the Toyota Research Lab in Cambridge, Massachusetts last year. The chore was more challenging than usual because I had to do it using a teleoperated pair of robotic arms with two-fingered pincers for hands. As I sat before the table, using a pair of controllers like bike handles with extra buttons and levers, I could feel the sensation of grabbing solid items, and also sense their heft as I lifted them, but it still took some getting used to. After several minutes tidying, I continued my tour of the lab and forgot about my brief stint as a teacher of robots. A few days later, Toyota sent me a video of the robot I'd operated sweeping up a similar mess on its own, using what it had learned from my demonstrations combined with a few more demos and several more hours of practice sweeping inside a simulated world.

"You put a car on the road which may be driving by the letter of the law, but compared to the surrounding road users, it's acting very conservatively. This can lead to situations where the autonomous car is a bit of a fish out of water," said Motional's Karl Iagnemma. Autonomous vehicles have control systems that learn how to emulate safe steering controls in a variety of situations based on real-world datasets of human driving trajectories. However, it is extremely hard to program the decision-making process given the infinite possible scenarios on real roads. Meanwhile, real-world data on "edge cases" (such as nearly crashing or being forced off the road) are hard to come by.

Dr. Carolyn Matl, Research Scientist at Toyota Research Institute, explains why Interactive Perception and soft tactile sensors are critical for manipulating challenging objects such as liquids, grains, and dough. She also dives into "StRETcH" a Soft to Resistive Elastic Tactile Hand, a variable stiffness soft tactile end-effector, presented by her research group. Carolyn Matl is a research scientist at the Toyota Research Institute, where she works on robotic perception and manipulation with the Mobile Manipulation Team. She received her B.S.E in Electrical Engineering from Princeton University in 2016, and her Ph.D. in Electrical Engineering and Computer Sciences at the University of California, Berkeley in 2021. At Berkeley, she was awarded the NSF Graduate Research Fellowship and was advised by Ruzena Bajcsy. Her dissertation work focused on developing and leveraging non-traditional sensors for robotic manipulation of complicated objects and substances like liquids and doughs. Would you mind introducing yourself? Thank you so much for having me on the podcast. I'm Carolyn Matl and I'm a research scientist at the Toyota research Institute where I work with a really great group of people on the mobile manipulation team on fun and challenging robotic perception and manipulation problems.

Ride-hailing company Lyft has sold off its autonomous vehicle unit to Toyota's Woven Planet Holdings subsidiary for $550 million, the latest in a string of acquisitions spurred by the cost and lengthy timelines to commercialize autonomous vehicle technology. Under the acquisition agreement announced Tuesday, Lyft's so-called Level 5 division will be folded into Woven Planet Holdings. Lyft will receive $550 million in cash, with $200 million paid upfront. The remaining $350 million will be made in payments over five years. About 300 people from Lyft Level 5 will be integrated into Woven Planet.

A study carried out jointly by Stanford University, SLAC National Accelerator Laboratory, the Massachusetts Institute of Technology, and the Toyota Research Institute (TRI) demonstrated the use of machine learning algorithms to understand the lifecycle of lithium-ion batteries. Until now, machine learning in battery technology was limited to identifying patterns in data to speed up scientific analysis. The latest discovery will help researchers in designing and developing longer-lasting batteries. The research team has been working to develop a long-lasting electric vehicle battery that can be charged in 10 minutes. "Battery technology is important for any type of electric powertrain. By understanding the fundamental reactions that occur within the battery we can extend its life, enable faster charging and ultimately design better battery materials. We look forward to building on this work through future experiments to achieve lower-cost, better-performing batteries," said Patrick Herring, a senior scientist of Toyota Research Institute.

Women in Robotics is a grassroots community involving women from across the globe. Our mission is supporting women working in robotics and women who would like to work in robotics. We formed an official 501c3 non-profit organization in 2020 headquartered in Oakland California. We'd like to introduce our 2021 Board of Directors: Andra Keay founded Women in Robotics originally under the umbrella of Silicon Valley Robotics, the non-profit industry group supporting innovation and commercialization of robotics technologies. Andra's background is in human-robot interaction and communication theory.

Inside an ordinary-looking home, a robot suspended from the ceiling slowly expands arms holding a sponge, before carefully wiping a kitchen surface clean. Nearby, another robot gently cleans a flat-screen television, causing it to wobble slightly. The cleaning robots live inside a mock home located at the Toyota Research Institute in Los Altos, California. The institute's researchers are testing a range of robot technologies designed to help finally realize the dream of a home robot. After looking at homes in Japan, which were often small and cluttered, the researchers realized they needed a creative solution.