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Fall 2024 GRASP SFI: Dian Wang, Northeastern University, “Equivariant Learning for Robotic Manipulation”
September 18 @ 3:00 pm - 4:00 pm
This was a hybrid event with in-person attendance in Levine 307 and virtual attendance…
ABSTRACT
Despite recent advances in machine learning for robotics, current approaches often lack sample efficiency, posing a significant challenge due to the enormous time consumption to collect real-robot data. In this talk, I will present our innovative methods that tackle this challenge by leveraging the inherent symmetries in the physical environment. Specifically, I will outline a comprehensive framework of equivariant policy learning and its application across various problem settings, including reinforcement learning, behavior cloning, and grasping. Our methods not only significantly outperform state-of-the-art baselines but also achieve these results with far less data, both in simulation and in real-world scenarios. Furthermore, our approach demonstrates robustness in the presence of symmetry distortions, such as variations in camera angles.
Presenter
Dian Wang
Dian Wang is a Ph.D. candidate at the Khoury College of Computer Sciences, Northeastern University, where he is co-advised by Prof. Robert Platt and Prof. Robin Walters. His research lies at the intersection of Machine Learning and Robotics, with a particular focus on Geometric Deep Learning and its applications in Robot Learning. Recently, Dian has focused on enhancing robotic manipulation through the use of equivariant methods to boost learning efficiency and performance. Dian has contributed to leading conferences and journals, including ICLR, NeurIPS, CoRL, ICRA, RSS, IJRR, AR, ISRR, and AAMAS. Dian was awarded the JPMorgan Ph.D. Fellowship in 2023 and the Khoury Research Fellowship in 2019.