Force-Driven Hook Disassembly via Constraint-Aware Object-Centric Reinforcement Learning

Nadeen Elhadad; Takuya Kiyokawa; Kensuke Harada

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arXiv · 2610.38513

Force-Driven Hook Disassembly via Constraint-Aware Object-Centric Reinforcement Learning

Nadeen Elhadad·Takuya Kiyokawa·Kensuke Harada

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Abstract

The growing volume of end-of-life products has intensified the need for automated disassembly, yet hook-type connections remain challengin: their orientations vary, they deform under load, and their geometry is hidden, so vision alone cannot identify a feasible non-destructive disassembly direction. This work proposes a constraint-aware reinforcement learning framework that infers feasible extraction directions online from contact forces. The policy is trained in a robot-agnostic, object-centric simulation on a simplified peg-in-hole pair and deployed zero-shot on a physical robot, where it transfers to real connections with unseen, multi-stage constraint structures. Because instantaneous contact forces are noisy and locally ambiguous, the policy is coupled with a particle filter belief that provides the policy with temporally integrated directional information and reduces redundant exploratory motion. The approach is validated on four real-world tasks, from peg-in-hole pairs to the inner propeller and front cover of an end-of-life air-conditioning unit. The full method achieves success rates of 100%, 100%, 90%, and 75% with the highest path efficiency on every task, and raises the success rate on the air-conditioning cover from 45% to 75% relative to a policy trained without the belief. These results indicate that force-driven reinforcement learning can enable disassembly without product-specific models or demonstrations.

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Nadeen Elhadad, Takuya Kiyokawa, Kensuke Harada. 2026-10-28. Force-Driven Hook Disassembly via Constraint-Aware Object-Centric Reinforcement Learning. https://arxiv.org/abs/2610.38513

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