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CAS Institute of Automation and Paxini collaborative study: tactile-enhanced reinforcement learning achieves sub-millimeter precision assembly

A team led by Cheng Long at the Institute of Automation, Chinese Academy of Sciences, published research at IROS 2026 using tactile-enhanced reinforcement learning to improve the success rate and stability of sub-millimeter precision insertion. The approach first uses Diffusion Policy imitation learning to reach the hole opening, then SAC reinforcement learning to complete the insertion, and handles real-world tactile noise through tactile baseline compensation and balanced sampling grouped by torque.

Part of the event: Tactile Reinforcement Learning at the Institute of Automation, Chinese Academy of Sciences Improves Sub-Millimeter Assembly Success Rate

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