SOURCE-LINKED INTELLIGENCE
InsertAnything: Generalizable Contact-Rich Precision Insertion from Simulation to Reality
Contact-rich precision insertion is a key manipulation skill in robotic assembly. Tight clearances make insertion more sensitive to alignment errors and prone to collisions and jamming, while variations in geometry and clearance across parts further complicate policy reuse. We present a reinforcement learning framework that trains insertion policies entirely in simulation for direct deployment without real-world demonstrations or policy fine-tuning. By combining target poses with compact three-dimensional fingertip force feedback, the policy learns to search for alignment and correct its motio
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-21T12:51:29.000Z
First collected: 2026-09-23T08:01:43.213Z. This is not the publication date.