This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom.

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.