MUCO: A Mani-Unit-Structured Manipulation Framework for Compositional Long-Horizon Robot Tasks
Overview
MUCO introduces a framework for long-horizon robot tasks using reusable manipulation units (MUs) with local goals and success criteria. It enables sequence orchestration and fallback mechanisms, outperforming baselines in success rates and error containment on benchmark tasks and real-world scenarios.
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- arXiv Robotics — research abstractsMUCO: A Mani-Unit-Structured Manipulation Framework for Compositional Long-Horizon Robot Tasks
This research proposes MUCO, a framework for long-horizon robot manipulation tasks that uses reusable manipulation units (MUs) defined by local goals, initiation conditions, and success/failure criteria. MUC, through MU-Centric Orchestration, constructs MU sequences, matches them to compatible policy models, and enables retry or fallback without re-executing verified steps. Evaluated on RLBench, RoboTwin 2.0, and real-world tasks, MUCO achieves higher success rates, better failure containment, and reduced error propagation compared to baselines.
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