Learning Cross-Hand Policies of High-DOF Reaching and Grasping

Abstract

Reaching-and-grasping is a fundamental skill for robotic manipulation, but existing methods usually train models on a specific gripper and cannot be reused on another gripper. In this paper, we propose a novel method that can learn a unified policy model that can be easily transferred to different dexterous grippers. Our method consists of two stages: a gripper-agnostic policy model that predicts the displacements of pre-defined key points on the gripper, and a gripper-specific adaptation model that translates these displacements into adjustments for controlling the grippers’ joints. The gripper state and interactions with objects are captured at the finger level using robust geometric representations, integrated with a transformer-based network to address variations in gripper morphology and geometry. In the experiments, we evaluate our method on several dexterous grippers and diverse objects, and the result shows that our method significantly outperforms the baseline methods. Pioneering the transfer of grasp policies across dexterous grippers, our method effectively demonstrates its potential for learning generalizable and transferable manipulation skills for various robotic hands.