Contact-Anchored Policies: Contact Conditioning Creates Strong Robot Utility Models

Language-conditioned robot learning has emerged as the dominant paradigm for creating general-purpose robot policies. However, language as an interface for robot control has inherent limitations: it is abstract, ambiguous, and lacks the spatial precision needed for manipulation tasks. We propose Contact-Anchored Policies (CAPs), which replace language conditioning with physical contact points in space. Rather than training a single generalist policy, CAPs organize capabilities as modular utility models, each specialized for a specific task. We also introduce EgoGym, a lightweight simulation environment for identifying and addressing failure modes before real-world deployment. Using only 23 hours of demonstration data, our method generalizes to novel environments and embodiments, outperforming large, state-of-the-art VLAs in zero-shot evaluations by 56%. All resources, including models, code, hardware specifications, simulation environments, and datasets, are publicly available.