Contact-Anchored Policies: Contact Conditioning Creates Strong Robot Utility Models
The prevalent paradigm in robot learning attempts to generalize across environments, embodiments, and tasks with language prompts at runtime. A fundamental tension limits this approach: language is often too abstract to guide the concrete physical understanding required for robust manipulation. In this work, we introduce Contact-Anchored Policies (CAP), which replace language conditioning with points of physical contact in space. Simultaneously, we structure CAP as a library of modular utility models rather than a monolithic generalist policy. This factorization allows us to implement a real-to-sim iteration cycle: we build EgoGym, a lightweight simulation benchmark, to rapidly identify failure modes and refine our models and datasets prior to real-world deployment. We show that by conditioning on contact and iterating via simulation, CAP generalizes to novel environments and embodiments out of the box on three fundamental manipulation skills while using only 23 hours of demonstration data, and outperforms large, state-of-the-art VLAs in zero-shot evaluations by 56%. All model checkpoints, codebase, hardware, simulation, and datasets will be open-sourced. Project page: https://cap-policy.github.io/
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Zichen Jeff Cui
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Omar Rayyan
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Bowen Tan
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Zavier Andrianarivo
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Zicheng Teng
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Yihang Zhou
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Krish Mehta
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Nicholas Wojno
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Kevin Yuanbo Wu
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Manan H Anjaria
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Ziyuan Wu
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Manrong Mao
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Guangxun Zhang
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Binit Shah
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Yejin Kim
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Nur Muhammad Mahi Shafiullah
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Mahi Shafiullah
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Contact-Anchored Policies: Contact Conditioning Creates Strong Robot Utility Models
The prevalent paradigm in robot learning attempts to generalize across environments, embodiments, and tasks with language prompts at runtime. A fundamental tension limits this approach: language is often too abstract to guide the concrete physical understanding required for robust manipulation. In this work, we introduce Contact-Anchored Policies (CAP), which replace language conditioning with points of physical contact in space. Simultaneously, we structure CAP as a library of modular utility models rather than a monolithic generalist policy. This factorization allows us to implement a real-to-sim iteration cycle: we build EgoGym, a lightweight simulation benchmark, to rapidly identify failure modes and refine our models and datasets prior to real-world deployment. We show that by conditioning on contact and iterating via simulation, CAP generalizes to novel environments and embodiments out of the box on three fundamental manipulation skills while using only 23 hours of demonstration data, and outperforms large, state-of-the-art VLAs in zero-shot evaluations by 56%. All model checkpoints, codebase, hardware, simulation, and datasets will be open-sourced. Project page: https://cap-policy.github.io/
Contact-Anchored Policies replace language conditioning with physical contact points and use modular utility models for robust manipulation, achieving superior zero-shot performance with minimal demonstration data through real-to-sim iteration cycles.
Contact-Anchored Policies language conditioning physical contact modular utility models real-to-sim iteration EgoGym manipulation skills zero-shot evaluation demonstration data