Beyond Context Sensitivity: Cross-Embodiment In-Context Policy Learning

Adapting robot policies to new tasks through post-training incurs demonstrationcollection and optimization costs. In-context learning (ICL) avoids test-time weight updates, but robot-reference methods still require demonstrations from the same embodiment. We ask whether human videos can provide references without robot teleoperation. Human-motion retargeting often relies on hand-designed kinematic correspondences, tying motion transfer to a robot-specific mapping. We introduce MIRAL (Mixed-embodiment In-context Reference Action Learning), a cross-embodiment ICL pretraining framework with a shared human–robot action interface. Disjoint action coordinates and modality-specific reference-action projectors support joint learning with human and robot references and queries. To discourage reference neglect, Context Error Margin (CEM) training fits query actions under matched references and raises mismatched prediction error. On The Imitator Game, we achieve 35% zero-shot success, 22 percentage points above the strongest listed comparator. Real-robot experiments on soybean pouring and tableware arrangement demonstrate effective human-reference-conditioned control.