Outcome-Sensitive Motion Search for Impact-Aware Dexterous Catching

2026-09-24

AI Summary

This study addresses the challenges in reinforcement learning for dexterous grasping, where coordinating interception with impact mitigation is difficult and teacher demonstrations are often imperfect. We propose an outcome-sensitive motion search method for constructing demonstration manifolds. By introducing the concept of an outcome-sensitive window, our approach integrates local geodesic search to optimize successful trajectories and rectify failure cases. Furthermore, a calibrated action error model is employed to validate action quality, thereby overcoming demonstration data bottlenecks. Experimental results demonstrate that the proposed method effectively mitigates the limitations of teacher policies, enabling the resulting policy to surpass privileged reinforcement learning teachers in both grasping success rate and impact mitigation performance.

Abstract

Skilled humans can catch fast-moving objects softly by coordinating interception, velocity matching, and follow-through to mitigate impact. Learning such impact-aware catching with reinforcement learning (RL), however, is challenging, as the policy must achieve reliable interception and grasping while regulating the sensitive transition into contact. Moreover, even a capable privileged-state RL teacher may not provide ideal demonstrations for a deployable imitation-learning (IL) student: teacher failures limit task coverage, while small variations in pre-contact motion can produce substantially different impact and grasping outcomes. We characterize this phenomenon through interventional outcome sensitivity and introduce the outcome-sensitive window (OSW) to guide targeted demonstration construction. Building on this formulation, we propose Outcome-Sensitive Motion Search, which learns a task-conditioned manifold of successful OSW motions and performs local geodesic search to refine successful teacher rollouts and repair task conditions where the teacher fails. We then validate candidate motions through complete rollouts under a calibrated IL-student action-error model and retain only successful executions as demonstrations. Extensive simulation experiments demonstrate that our method effectively repairs task conditions where the teacher fails and enables the resulting IL policy to outperform the privileged RL teacher in both catching success and impact mitigation.

Problem

• dexterous catching
• impact mitigation
• reinforcement learning
• imitation learning
• outcome sensitivity

Innovation

• Outcome-Sensitive Motion Search
• Impact-Aware Dexterous Catching
• Imitation Learning
• Interventional Outcome Sensitivity
• Reinforcement Learning