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Zero-Shot Whole-Body Humanoid Control Via Behavioral Foundation Models by louis.bagot.nao is a document available to read on EtoBox.

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This paper introduces a novel algorithm called Forward-Backward Representations with Conditional-Policy Regularization (FB-CPR) for unsupervised reinforcement learning, which enables humanoid agents to perform a variety of tasks through zero-shot inference. The method utilizes unlabeled behavior datasets to train a behavioral foundation model (M ETA M OTIVO), allowing it to express human-like behaviors and achieve competitive performance in whole-body control tasks. The results demonstrate that this approac

Author

louis.bagot.nao

Language

EN