Transferability in Robotics Workshop

Aleksandra Faust

Talk title: Toward Generalist Agents

Bio: Aleksandra Faust is a Senior Staff Research Scientist, Autonomous Agents research lead, and Reinforcement Learning research team co-founder at Google Brain. Her research is centered around safe and scalable autonomous systems for social good, including reinforcement learning, planning, and control for robotics, autonomous driving, and digital assistants. Previously, Aleksandra founded and led Task and Motion Planning research in Robotics at Google, machine learning for self-driving car planning and controls in Waymo, and was a senior researcher in Sandia National Laboratories. She earned a Ph.D. in Computer Science at the University of New Mexico with distinction, and a Master's in Computer Science from the University of Illinois at Urbana-Champaign. Aleksandra won the IEEE RAS Early Career Award for Industry, the Tom L. Popejoy Award for the best doctoral dissertation at the University of New Mexico in the period of 2011-2014, and was named Distinguished Alumna by the University of New Mexico School of Engineering. Her work has been featured in the New York Times, PC Magazine, ZdNet, VentureBeat, and ​was awarded Best Paper in Service Robotics at ICRA 2018, Best Paper in Reinforcement Learning for Real Life (RL4RL) at ICML 2019, Best Paper of IEEE Computer Architecture Letters in 2020, and IEEE Micro Top Picks 2023 Honorable Mention.

Aleš Ude

Talk title: Enhancing training datasets for data-driven motion prediction

Bio: Aleš Ude received the Diploma degree in applied mathematics from the University of Ljubljana, Slovenia, and the Dr. eng. sciences degree from the University of Karlsruhe, Germany, in 1990 and 1995, respectively. Currently he is the head of the Dept. of Automatics, Biocybernetics, and Robotics, Jožef Stefan Institute, Ljubljana. His research interests include robot learning, imitation learning, reconfigurable robotic systems, cognitive robotics, and humanoid robotics. He has been a coordinator and/or principal investigator of numerous national and international projects in these areas.

Alin Albu-Schäffer

Talk title: euROBIN: The European Robotics and AI Network

Bio: Alin Albu-Schäffer received his M.S. in electrical engineering from the Technical University of Timisoara, Romania in 1993 and his Ph.D. in automatic control from the Technical University of Munich in 2002. Since 2012 he is the head of the Institute of Robotics and Mechatronics at the German Aerospace Center (DLR). Moreover, he is a professor at the Technical University of Munich, holding the Chair for "Sensor Based Robotic Systems and Intelligent Assistance Systems" at the Computer Science Department. His personal research interests include robot design, modeling and control, nonlinear control, flexible joint and variable compliance robots, impedance and force control, physical human-robot interaction, bio-inspired robot design and control. He received several awards, including the IEEE King-Sun Fu Best Paper Award of the Transactions on Robotics in 2012 and 2014; several ICRA and IROS Best Paper Awards as well as the DLR Science Award. He was strongly involved in the development of the DLR light-weight robot and its commercialization through technology transfer to KUKA. He is the coordinator of euROBIN, the European network of excellence on intelligent robotics.

Animesh Garg

Talk title: Prospection: rethinking transfer in the era of large scale pretraining

Bio: Animesh Garg is an Assistant Professor of Computer Science at University of Toronto and a Faculty Member at the Vector Institute. He directs the UofT People, AI and Robotics (PAIR) group. Animesh is affiliated with Mechanical and Industrial Engineering (courtesy) and UofT Robotics Institute. He is also a Sr. Research Scientist at Nvidia.

David Hsu

Talk title: Generalization and Transfer in Deformable Object Manipulation

Bio: David Hsu is a professor of computer science and the Director of Smart Systems Institute at the National University of Singapore (NUS). He is an IEEE Fellow. He received BSc in Computer Science & Mathematics from the University of British Columbia and PhD in computer science from Stanford University. At NUS, he co-founded NUS Advanced Robotics Center in 2013 and founded the AI Laboratory in 2019. His research spans robotics, AI, and computational structural biology. In recent years, he has been working on robot planning and learning under uncertainty for human-centered robots. His work won several international awards, including, most recently, Test of Time Award at Robotics: Science & Systems (RSS), 2021. He has chaired or co-chaired several major international robotics conferences, including CoRL 2021, ICRA 2016, RSS 2015, and WAFR 2004 and 2010.

Eric Eaton

Talk title: Composable Representations for Lifelong Learning in Autonomous Systems

Bio: Eric Eaton is a research associate professor in the Department of Computer and Information Science at the University of Pennsylvania, and a member of the GRASP (General Robotics, Automation, Sensing, & Perception) lab. He also has a secondary appointment in biomedical and health informatics at Children’s Hospital of Philadelphia. Prior to joining Penn, he was a visiting assistant professor at Bryn Mawr College, a senior research scientist at Lockheed Martin Advanced Technology Laboratories, and part-time faculty at Swarthmore College. His primary research interests lie in the field of machine learning and interactive AI, with applications to service robotics and personalized medicine. In particular, his research focuses on developing versatile AI systems that can learn multiple tasks over a lifetime of experience in complex environments, transfer learned knowledge to rapidly acquire new abilities, and collaborate effectively with humans and other agents through interaction.

Gayane Kazhoyan

Talk title: Transferability in Task-level Robot Control for Everyday Mobile Manipulation Actions

Bio: Gayane is a research associate at the Institute for Artificial Intelligence at the University of Bremen, lead by Michael Beetz. Her research focus is on task-level robot control and plan executives. She has more than 10 years of experience in mobile manipulation with physical robots such as the PR2, Tiago, HSR, Turtlebot, LWR, UR and a multitude of simulated robots. The main application areas are everyday actions in a household, mobile pick and place in retail and assembly. Prior to joining Michael Beetz's group in November 2013, she worked for one year as a researcher at Kastanienbaum GmbH, now Franka Emika, led by Sami Haddadin, in tight collaboration with the Robotics and Mechatronics Center of DLR. Gayane has a M.Sc. degree in Informatics with a major in AI and Robotics from the Technical University of Munich and a B.Eng. degree in Informatics with a major in Information Security from the State Engineering University of Armenia.

Noémie Jaquier

Talk title: A Geometric Take on Embodiment Transfer

Bio: Noémie Jaquier is a postdoctoral researcher at the High Performance Humanoid Technologies Lab (H²T) at the Karlsruhe Institute of Technology (KIT), Germany. From 2016 to 2020, she was a PhD student affiliated to the Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland and working at the Idiap Research Institute. She did a six-months PhD sabbatical in the Bosch Center for Artificial Intelligence (BCAI) in Germany in 2019. Her research brings a novel Riemannian perspective to robot learning, optimization, and control by leveraging Riemannian geometry as inductive bias and as a theory to provide sound theoretical guarantees. She investigates data-efficient methods that build on geometric spaces and exploit the geometric information naturally arising in robotic data. Her work focuses on skills learning via human demonstrations and adaptation techniques with geometry as a cornerstone. Noémie Jaquier received the best presentation award at the 2019 Conference on Robot Learning and her PhD thesis was nominated for the EPFL Asea Brown Boveri Ldt. Award.