用语义接触信息实现机器人装配任务的实时自适应。
CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation
- 通过6维语义接触上下文建模，替代原始参数调优。
- 在真实机械臂上实现无演示、无梯度的在线适应，成功率更高。
- 适合需要高精度力控的装配任务，如插销、螺纹拧入等。
我们提出CoRMA（对比式机器人运动自适应），一种基于上下文的元自适应框架，用于力主导型装配任务。CoRMA将原始模拟器参数适应替换为一个紧凑的6维仅模拟器语义接触上下文，描述接触触发、侧向耦合、引导过渡、接触方向和卡滞状态。一个可部署的因果Transformer适配器，通过语义回归和力域对比目标，从力觉、本体感知和动作历史中在线推断该上下文。部署时移除真实上下文，改用推断出的上下文，实现无需演示、特权输入或梯度更新的单次实验内自适应。我们在Isaac Lab / Isaac Sim 5.0上的PegInsert、GearMesh、NutThread任务及真实Marvin机械臂上评估了CoRMA。相比实现高仿真成功率但硬件表现严重下降的FORGE基线，CoRMA在受控目标姿态噪声下仍保持更高的真实验证成功率。结果表明，语义接触推断可作为相关装配任务族中的可复用自适应接口，而更广泛未见任务泛化与真实到仿真校准仍是未来工作。
原文摘要 · Abstract (English)
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.
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