LATENT: Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data
Introduces a three-stage pipeline that extracts a latent action space from low-quality human tennis demonstrations, then trains a high-level policy in simulation via reinforcement learning. Enables dynamic whole-body humanoid tennis play with back-and-forth volleys at human level.
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