Videos: Robots With Knives, Powerline Drones, Exoskeletons

Greetings from the
IEEE International Conference on Robotics and Automation (ICRA) in Yokohama, Japan! We hope you’ve been enjoying our short videos on TikTok, YouTube, and Instagram. They are just a preview of our in-depth ICRA coverage, and over the next several weeks we’ll have lots of articles and videos for you. In today’s edition of Video Friday, we bring you a dozen of the most interesting projects presented at the conference.

Enjoy today’s videos, and stay tuned for more ICRA posts!

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ICSR 2024: 23–26 October 2024, ODENSE, DENMARK

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The following two videos are part of the “
Cooking Robotics: Perception and Motion Planning” workshop, which explored “the new frontiers of ‘robots in cooking,’ addressing various scientific research questions, including hardware considerations, key challenges in multimodal perception, motion planning and control, experimental methodologies, and benchmarking approaches.” The workshop featured robots handling food items like cookies, burgers, and cereal, and the two robots seen in the videos below used knives to slice cucumbers and cakes. You can watch all workshop videos here.

“SliceIt!: Simulation-Based Reinforcement Learning for Compliant Robotic Food Slicing,” by Cristian C. Beltran-Hernandez, Nicolas Erbetti, and Masashi Hamaya from OMRON SINIC X Corporation, Tokyo, Japan.

Cooking robots can enhance the home experience by reducing the burden of daily chores. However, these robots must perform their tasks dexterously and safely in shared human environments, especially when handling dangerous tools such as kitchen knives. This study focuses on enabling a robot to autonomously and safely learn food-cutting tasks. More specifically, our goal is to enable a collaborative robot or industrial robot arm to perform food-slicing tasks by adapting to varying material properties using compliance control. Our approach involves using Reinforcement Learning (RL) to train a robot to compliantly manipulate a knife, by reducing the contact forces exerted by the food items and by the cutting board. However, training the robot in the real world can be inefficient, and dangerous, and result in a lot of food waste. Therefore, we proposed SliceIt!, a framework for safely and efficiently learning robot food-slicing tasks in simulation. Following a real2sim2real approach, our framework consists of collecting a few real food slicing data, calibrating our dual simulation environment (a high-fidelity cutting simulator and a robotic simulator), learning compliant control policies on the calibrated simulation environment, and finally, deploying the policies on the real robot.

“Cafe Robot: Integrated AI Skillset Based on Large…