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CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation
Wentian Wang · 2026-05-23 · via cs.LG updates on arXiv.org

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Abstract:We present CoRMA(Contrastive Robotic Motor Adaptation), a context-based meta-adaptation framework that modifies RMA for force-dominant assembly. CoRMA replaces raw simulator-parameter adaptation with a compact 6D simulator-only semantic contact context describing contact onset, lateral engagement, guided transition, contact direction, and jamming. A deployable causal Transformer adapter infers this context online from force, proprioceptive, and action histories using semantic regression and a force-regime contrastive objective. At deployment, oracle context is removed and replaced by the inferred context, enabling within-episode adaptation without demonstrations, privileged inputs, or gradient updates. We evaluate CoRMA on PegInsert, GearMesh, and NutThread in Isaac Lab / Isaac Sim~5.0 and on a real Marvin arm. Compared with FORGE baselines that achieve high simulation success but degrade substantially on hardware, CoRMA retains higher verified real success under controlled target-pose noise. These results support semantic contact inference as a reusable adaptation interface within a related assembly task family, while broader unseen-task generalization and Real2Sim calibration remain future work.
Subjects: Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2605.22082 [cs.RO]
  (or arXiv:2605.22082v1 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2605.22082

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Wentian Wang [view email]
[v1] Thu, 21 May 2026 07:21:56 UTC (7,741 KB)