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

1 reports1 reporting sourcesUpdated 1 days ago

Overview

Report summary

This research proposes a reinforcement learning framework that uses contact forces to infer feasible disassembly directions for hook-type connections. The method is validated on real-world tasks, achieving success rates of 100%, 100%, 90%, and 75% with improved path efficiency, and significantly raises the success rate on an air-conditioning cover task compared to a baseline policy.

From arXiv Robotics — research abstracts

Event evidence and corrections

0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.

Reported quantity · Reported success rate: 100 percent · Basis not reported
Supporting report

“elief 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 rat”

Exact source · revision 1

Source owner not reported

Reported quantity · task variety: 4 other · Basis not reported
Supporting report

“elief 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 rat”

Exact source · revision 1

Source owner not reported

Evidence dependency changes (1)
  • 2026-10-31T04:02:21.265Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.

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10/30
  1. arXiv Robotics — research abstractsSignal
    Force-Driven Hook Disassembly via Constraint-Aware Object-Centric Reinforcement Learning

    This research proposes a reinforcement learning framework that uses contact forces to infer feasible disassembly directions for hook-type connections. The method is validated on real-world tasks, achieving success rates of 100%, 100%, 90%, and 75% with improved path efficiency, and significantly raises the success rate on an air-conditioning cover task compared to a baseline policy.

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