Learning Cross-hand Policies for High-DOF Reaching and Grasping
arxiv(2024)
摘要
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 without retraining. 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 predefined 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
experimental part, we evaluate our method on several dexterous grippers and
objects of diverse shapes, and the result shows that our method significantly
outperforms the baseline methods. Pioneering the transfer of grasp policies
across different dexterous grippers, our method effectively demonstrates its
potential for learning generalizable and transferable manipulation skills for
various robotic hands
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