Towards Unified Robotics Manipulation via Object-Centric Policy

Abstract: Enabling robots to manipulate everyday objects is a key focus in embodied intelligence research. This area presents challenges due to the diversity of tasks and the varying shapes and structures of objects. An ideal robotic manipulation policy should provide a unified representation that is adaptable to different objects and tasks. In this presentation, we introduce the concept of object-centric manipulation policy through affordance learning, which offers a unified way to represent policies that are suitable for a wide range of objects and tasks. By leveraging affordance, we decouple the relationship between robots and objects, enabling different robots to perform the same tasks using the same action trajectories.

Bio: Hao Dong is an Assistant Professor at Peking University studying AI, robotics, and vision. His current research focuses on several exciting areas: generalizable robotic manipulation, dexterous manipulation, robot vision, and autonomous decision-making. The ultimate goal is to create cost-effective and autonomous robots for both industrial applications and home assistance scenarios.