Learning Stable In-Grasp Manipulation in a Non-Dropping Action Space

2026-06-26

AI Summary

This work addresses the limitations of traditional dexterous manipulation controllers, which rely on strongly assumed analytical models, and end-to-end reinforcement learning approaches, which often suffer from objective conflicts and training instability. The authors propose a skill decomposition framework that integrates physical and control-theoretic priors to decouple in-hand manipulation into analytically tractable subcomponents. By embedding theoretical constraints within each subcomponent to guide learning, this method systematically incorporates classical control knowledge into the learning pipeline for dexterous manipulation. Evaluated across diverse objects, sensor noise levels, actuation delays, and friction conditions, the approach significantly enhances policy learning stability, sample efficiency, and generalization, enabling efficient and precise in-hand repositioning and reorientation.

Abstract

Traditionally, dexterous manipulation controllers are designed using analytic models constrained by strong assumptions about the hand and the objects being manipulated. Reinforcement learning (RL) has become another common approach in which skills are explored openly in an end-to-end manner but is inefficient because of unnoticeable instability and conflicts in learning objectives. This paper attempts to efficiently explore stable and accurate manipulation skills by decomposing dexterous skills into multiple simpler/analyzable components. Each skill component is subsequently learned with constraints and guidance from classical physics and control theory. Our work shows that for stable grasp, in-grasp reposition/reorientation with different objects, sensor/motor noise, latency, and frictional conditions, skill learning becomes efficient and stable with prior knowledge from theory.

Problem

• dexterous manipulation
• stable grasp
• in-grasp repositioning
• reinforcement learning
• skill decomposition

Innovation

• dexterous manipulation
• skill decomposition
• physics-informed learning
• stable grasping
• reinforcement learning