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About this paper
Zhirui Fang, Qingchi Yu, Ziyang Chen, Longfei Li, Haoran Ma, Keru Zhou, Xinrun Xu, Samith Va and 13 more
EMERGE-Policy coordinates long-horizon robot manipulation through a Main Agent that delegates perception and checking, calls skills to act, predict outcomes, and evaluate progress. Sub Agents return structured findings. On LIBERO-Plus, average success across seven perturbation categories is 93.9%; removing Evaluation lowers it to 85.6%, supporting its role in this system, not proving isolated agents outperform a matched single-agent design.
Key insights
- An effective robot 'mind' can emerge from the coordinated interaction of specialized components rather than being centralized in a single, monolithic policy.
- Explicitly managing information flow and delegating evidence-intensive operations to role-specific Sub Agents prevents high-level planners from information overload.
- Categorizing robot capabilities into Imagination and Evaluation Skills enables principled action selection and proactive failure mitigation through forward prediction and outcome verification before physical commitment.
Summary by alphaXiv.
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