In this talk, I will investigate the problem of learning manipulation skills across a diverse set of robotic embodiments. Conventionally, manipulation skills are learned separately for every task, environment and robot. However, in domains like Computer Vision and Natural Language Processing we have seen that one of the main contributing factors to generalisable models is large amounts of diverse data. If we were able to have one robot learn a new task even from data recorded with a different robot, then we could already scale up training data to a much larger degree for each robot embodiment. In this talk, I will present a new, large-scale datasets that was put together across multiple industry and academic research labs to make it possible to explore the possibility of cross-embodiment learning in the context of robotic manipulation, alongside experimental results that provide an example of effective cross-robot policies. Given this dataset, I will also present multiple alternative ways to learn cross-embodiment policies. These example approaches will include (1) UniGrasp - a model that allows to synthesise grasps with new hands, (2) XIRL - an approach to automatically discover and learn vision-based reward functions from cross-embodiment demonstration videos and (3) Equivact - an approach that leverages equivariance to learn sensorimotor policies that generalise to scenarios that are traditionally out-of-distribution.
Bio: Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University. She was a group leader at the Autonomous Motion Department (AMD) of the MPI for Intelligent Systems until September 2017. Before joining AMD in January 2012, Jeannette Bohg was a PhD student at the Division of Robotics, Perception and Learning (RPL) at KTH in Stockholm. In her thesis, she proposed novel methods towards multi-modal scene understanding for robotic grasping. She also studied at Chalmers in Gothenburg and at the Technical University in Dresden where she received her Master in Art and Technology and her Diploma in Computer Science, respectively. Her research focuses on perception and learning for autonomous robotic manipulation and grasping. She is specifically interested in developing methods that are goal-directed, real-time and multi-modal such that they can provide meaningful feedback for execution and learning. Jeannette Bohg has received several Early Career and Best Paper awards, most notably the 2019 IEEE Robotics and Automation Society Early Career Award and the 2020 Robotics: Science and Systems Early Career Award.
PhD students and postdocs can signup to join Jeannette for lunch. There will also be a Robotics Social at 4:00 PM in the F-Wing Cafe Area - all are welcome to attend!
