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CAS Institute of Automation proposes tactile-enhanced grasp-and-insertion framework, achieving 67% success rate at 0.05 mm clearance

The team led by Cheng Long at the Institute of Automation, Chinese Academy of Sciences, proposed a tactile-enhanced full-pipeline learning framework for grasping and insertion, achieving a 67% success rate at 0.05 mm clearance and a 60% reduction in peak contact force; the paper was accepted to IROS 2026. The research uses the PaXini PX6AX GEN3 multi-dimensional tactile sensor as its core sensing hardware, integrated into a seven-axis robotic arm, covering five geometric shapes and three clearance levels of 1, 0.25, and 0.05 mm. Under the same conditions, the tactile-free IL+RL approach achieved a 52% success rate, while the pure imitation learning approach achieved 0.

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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