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This work proposes Action Upcycling, a training-free algorithm that reuses actions the policy would otherwise discard, without accessing model internals or drawing extra samples, and finds that discarded actions stay close to their replanned versions as long as the action velocity remains smooth.
DPP enables real-time dynamic manipulation on a single consumer GPU without additional training on dynamic data and constructs a counterfactual observation that places a predicted target position in a familiar robot context, allowing the model to invoke an existing manipulation skill rather than generate a recovery beh...
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