Date and time:
August 13 at 7:30am-8:30am (PDT) 10:30am (EDT) 4:30PM (CEST) 10:30pm (HKT)
Live Stream: Zoom Webinar (https://zoom.us/j/99050915808)
Live questions and discussion: Slido (https://app.sli.do/event/f6ls93vv)
Moderator: David Hsu
Panelists: Aude Billard, Sergey Levine, Russ Tedrake, Michael Wang
Title: A Conversation on the Roles of Physics-Based Models and Data-Driven Learning in Robotics
Video on Bilibili: https://www.bilibili.com/video/BV1Lv411i7qp/
Video on YouTube: hhttps://youtu.be/ftwIWLZOtog
There is an ongoing debate in the robotics community on the intertwined relationship between model-based robotics and the more recent data-driven robotics. Robotics research has provided a wealth of powerful models for perception, estimation, planning, compliant strategies, and control and its guarantees. Put together, they form the body of science that powers many successful real-world robotics applications today. Over the past decade, data-driven techniques, particularly, deep learning, have produced exciting results in areas such as computer vision, natural language understanding, and 3D geometry processing, all critical in robotics. The complexity of challenging real-world tasks requires robotics systems with spatial, temporal, physical, and social understanding that can be best addressed by a broad range of methodologies and tools. Nascent research is showing promising synergy by combining model-based and data-driven robotics.
This panel aims to bring together different perspectives to help untangle the relationship between models and data, and shed light on key questions for future robotics research, particularly on how data-driven learning and physics-based models can complement each other in robotics system?
David Hsu (Moderator)
David Hsu (Moderator)
David Hsu is a Provost's Chair Professor in the Department of Computer Science, National University of Singapore (NUS) and a member of NUS Graduate School for Integrative Sciences & Engineering, director of NUS AI Laboratory (NUSAIL) and co-director of NUS Advanced Robotics Center. He held visiting positions in MIT Aeronautics & Astronautics Department and CMU Robotics Institute. He is an IEEE Fellow. His research interests span robotics, AI, and computational structural biology. In recent years, he has been working on robot planning and learning under uncertainty and human-robot collaboration. He and his team won the Humanitarian Robotics and Automation Technology Challenge Award at IEEE ICRA 2015, the RoboCup Best Paper Award at IEEE IROS 2015, and the Best Systems Paper Award at RSS 2017.
Aude Billard is full professor and head of the LASA laboratory at the School of Engineering at the Swiss Institute of Technology Lausanne (EPFL). She was a faculty member at the University of Southern California, prior to joining EPFL in 2003. She was the recipient of the Intel Corporation Teaching award, the Swiss National Science Foundation career award in 2002, the Outstanding Young Person in Science and Innovation from the Swiss Chamber of Commerce and the IEEE-RAS Best Reviewer Award. Her research spans the fields of machine learning and robotics with a particular emphasis on learning from sparse data and performing fast and robust retrieval. Her work finds application to robotics, human-robot / human-computer interaction, and computational neuroscience. This research received best paper awards from IEEE T- RO, RSS, ICRA, IROS, Humanoids and ROMAN and was featured in premier venues (BBC, IEEE Spectrum, Wired).
Sergey Levine is an Assistant Professor of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as computer vision and graphics. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more. His work has been featured in many popular press outlets, including the New York Times, the BBC, MIT Technology Review, and Bloomberg Business.
Russ is the Toyota Professor of Electrical Engineering and Computer Science, Aeronautics and Astronautics, and Mechanical Engineering at MIT, the Director of the Center for Robotics at the Computer Science and Artificial Intelligence Lab, and the leader of Team MIT’s entry in the DARPA Robotics Challenge. Russ is also the Vice President of Robotics Research at the Toyota Research Institute. He is a recipient of the NSF CAREER Award, the MIT Jerome Saltzer Award for undergraduate teaching, the DARPA Young Faculty Award in Mathematics, the 2012 Ruth and Joel Spira Teaching Award, and was named a Microsoft Research New Faculty Fellow.
Michael Yu Wang is a Chair Professor and the Founding Director of Robotics Institute at Hong Kong University of Science and Technology. He earned his PhD from Carnegie Mellon University and previously taught at University of Maryland, Chinese University of Hong Kong, and National University of Singapore. He has numerous professional honors–including Ralph R. Teetor Educational Award from Society of Automotive Engineers, 1994; LaRoux K. Gillespie Outstanding Young Manufacturing Engineer Award from Society of Manufacturing Engineers, 1995; Boeing–A.D. Welliver Faculty Summer Fellow, 1998; China State Natural Science Prize (Second Class) from the Ministry of Science & Technology of China (2012), and ASME Design Automation Award (2013) from ASME. He is a Fellow of ASME, HKIE, and IEEE.