LATENT: Teaching a Humanoid to Play Tennis from Imperfect Data

from Embodied AI 101 · host Shaoqing Tan

Introduces a three-stage pipeline that extracts a latent action space from noisy, low-quality human motion capture, then trains a high-level RL policy in simulation to compose and execute dynamic whole-body tennis skills. Achieves volleys at human-level performance on a humanoid robot.

Episode metadata supplied by the publisher feed · Published May 19, 2026

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LATENT: Teaching a Humanoid to Play Tennis from Imperfect Data

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