Coarse-to-Fine pOlicy refinement process combined with a Holistic-Local contrastive Representation (CFOHLR) method to enable effective zero-shot policy adaptation. Specifically, we utilize task language instructions as prior knowledge to select different parameterized modules as a coarse policy. This coarse policy is subsequently refined by a fine policy generated through a hypernetwork, which produces a task-aware policy based on task representations. Additionally, since task representations can influence the effectiveness of task-aware policies, we employ contrastive learning from both holistic and local perspectives to enhance these representations for more effective policy generation. Experimental results demonstrate that our method significantly improves learning efficiency and zero-shot adaptation on new tasks, outperforming previous methods on the Meta-World ML-10 and ML-45 benchmarks.">

Zero-Shot Adaptation at Task-Level via Coarse-to-Fine Policy Refinement and Holistic-Local Contrastive Representation (original) (raw)

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