Tac2Motion: Contact-Aware Reinforcement Learning with Tactile Feedback for Robotic Hand Manipulation

2025-09-22

AI Summary

Dexterous manipulation tasks with high contact complexity—such as bottle cap opening—pose significant challenges for robotic learning due to sparse rewards, high-dimensional tactile feedback, and stringent requirements on grasp stability and multi-finger coordination. Method: We propose a tactile-aware reinforcement learning framework that integrates haptic sensing with policy optimization. Key components include a tactile-guided reward shaping mechanism explicitly modeling stable grasping and coordinated multi-finger motion, and an embedded multimodal observation space jointly encoding tactile signals as both reward components and critical state inputs. Contribution/Results: The method achieves superior data efficiency and policy robustness, converging rapidly in simulation and transferring zero-shot to a Shadow Robot dexterous hand for successful real-world cap opening. Experiments demonstrate strong generalization across diverse object geometries and dynamic disturbances. This work delivers a deployable, end-to-end solution for tactile-driven dexterous manipulation.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

This paper proposes Tac2Motion, a contact-aware reinforcement learning framework to facilitate the learning of contact-rich in-hand manipulation tasks, such as removing a lid. To this end, we propose tactile sensing-based reward shaping and incorporate the sensing into the observation space through embedding. The designed rewards encourage an agent to ensure firm grasping and smooth finger gaiting at the same time, leading to higher data efficiency and robust performance compared to the baseline. We verify the proposed framework on the opening a lid scenario, showing generalization of the trained policy into a couple of object types and various dynamics such as torsional friction. Lastly, the learned policy is demonstrated on the multi-fingered robot, Shadow Robot, showing that the control policy can be transferred to the real world. The video is available: https://youtu.be/poeJBPR7urQ.

Problem

• Learning contact-rich in-hand manipulation tasks using tactile feedback
• Improving robotic manipulation through contact-aware reinforcement learning
• Enabling robust lid removal with tactile sensing and reward shaping

Innovation

• Contact-aware reinforcement learning with tactile feedback
• Tactile sensing-based reward shaping for manipulation
• Embedding tactile sensing into the observation space