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EDAR: Learning Environment-Dependent Action Representations for Robotic Manipulation
Learning effective action representations is critical for robotic manipulation, where raw control trajectories are often noisy, redundant, and difficult to model directly. Existing methods mainly encode the structure of the action stream itself, treating the role of actions in the environment as imp
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