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FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty

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arxiv 2408.04587 v2 pith:NTXBCC2Z submitted 2024-08-08 cs.RO

Michael Noseworthy , Bingjie Tang , Bowen Wen , Ankur Handa , Chad Kessens , Nicholas Roy , Dieter Fox , Fabio Ramos
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Yashraj Narang Iretiayo Akinola
This is my paper
classification cs.RO
keywords forgepoliciescontact-richforcerobustassemblygearinsertion
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We present FORGE, a method for sim-to-real transfer of force-aware manipulation policies in the presence of significant pose uncertainty. During simulation-based policy learning, FORGE combines a force threshold mechanism with a dynamics randomization scheme to enable robust transfer of the learned policies to the real robot. At deployment, FORGE policies, conditioned on a maximum allowable force, adaptively perform contact-rich tasks while avoiding aggressive and unsafe behaviour, regardless of the controller gains. Additionally, FORGE policies predict task success, enabling efficient termination and autonomous tuning of the force threshold. We show that FORGE can be used to learn a variety of robust contact-rich policies, including the forceful insertion of snap-fit connectors. We further demonstrate the multistage assembly of a planetary gear system, which requires success across three assembly tasks: nut threading, insertion, and gear meshing. Project website can be accessed at https://noseworm.github.io/forge/.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation

    cs.RO 2026-05 unverdicted novelty 7.0 of 10

    CoRMA enables within-episode adaptation for contact-rich robotic assembly by inferring semantic contact context with a causal Transformer and force-regime contrastive objective, retaining higher real success than FORG...

  2. Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertainty

    cs.RO 2026-04 unverdicted novelty 7.0 of 10

    MATCH trains hybrid position-force RL policies that achieve up to 10% higher success rates and 5x fewer breaks than pose-only policies in fragile peg-in-hole tasks under localization uncertainty, with strong sim-to-re...

  3. FORGE-plus: Force-Budgeted Recovery for Contact-Rich Assembly with a Frozen LLM Supervisor

    cs.RO 2026-07 conditional novelty 6.0 of 10

    With a hidden per-episode breaking force, an LLM-set force ceiling plus force-signature recovery achieves 256/256 clean insertions on fragile and robust parts and resolves 40–64% of injected jams in simulation.

  4. CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation

    cs.RO 2026-05 unverdicted novelty 6.0 of 10

    CoRMA modifies RMA by replacing raw parameter adaptation with inference of a 6D semantic contact context via a causal Transformer trained with semantic regression and force-regime contrastive loss, yielding higher rea...

  5. Cross-Entropy Optimization of Physically Grounded Task and Motion Plans

    cs.RO 2025-12 conditional novelty 6.0 of 10

    A TAMP method that optimizes controller parameters with cross-entropy search in a parallel physics simulator, demonstrated on contact-exploiting pick-and-place and pushing tasks.

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