Master Micro Residual Correction with Adaptive Tactile Fusion and Force-Mixed Control for Contact-Rich Manipulation
Xingting Li, Yifan Xie, Han Liu, Wei Hou, Guangyu Chen, Shoujie Li, Wenbo Ding

TL;DR
This paper introduces M2-ResiPolicy, a hierarchical control framework combining high-level diffusion-based guidance with low-level tactile feedback correction, improving contact-rich manipulation tasks in robotics.
Contribution
The paper presents a novel Master-Micro residual control architecture that integrates adaptive tactile fusion and force-mixed control for enhanced manipulation performance.
Findings
Achieves 93% damage-free success in chip grasping.
Outperforms standard diffusion and reactive policies.
Demonstrates superior force regulation stability.
Abstract
Robotic contact-rich and fine-grained manipulation remains a significant challenge due to complex interaction dynamics and the competing requirements of multi-timescale control. While current visual imitation learning methods excel at long-horizon planning, they often fail to perceive critical interaction cues like friction variations or incipient slip, and struggle to balance global task coherence with local reactive feedback. To address these challenges, we propose M2-ResiPolicy, a novel Master-Micro residual control architecture that synergizes high-level action guidance with low-level correction. The framework consists of a Master-Guidance Policy (MGP) operating at 10 Hz, which generates temporally consistent action chunks via a diffusion-based backbone and employs a tactile-intensity-driven adaptive fusion mechanism to dynamically modulate perceptual weights between vision and…
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Taxonomy
TopicsRobot Manipulation and Learning · Teleoperation and Haptic Systems · Motor Control and Adaptation
