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Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data

Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data

Jingzhi Cui, Zhexiong Wang, Bangjie Xu, Pengyu Zhao, Youyuan Li, Zhi Su, Peng Ren, Mengdi Xu, Chao Yu, Yi Wu, Luyang Wang, Zhongyu Li · N/A · 2026

reinforcement-learning
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Summary

High-speed racket sports provide a demanding testbed for humanoid robots, requiring time-critical decisions, precise striking, and dynamic whole-body coordination.

Abstract Summary

High-speed racket sports provide a demanding testbed for humanoid robots, requiring time-critical decisions, precise striking, and dynamic whole-body coordination. In badminton, fast-changing shuttle trajectories require timely contact decisions, while successful returns demand precise racket pose and velocity within a brief contact window and across a broad three-dimensional striking workspace. Human motion data provide valuable priors for such athletic skills, but usable badminton references are limited and imperfect. Direct tracking provides insufficient executable variation for diverse shuttle conditions, while purely task-driven optimization may produce unnatural motion. To address these challenges, we present a three-stage hierarchical reinforcement learning framework for dynamic humanoid badminton. First, task-randomized motion augmentation expands sparse annotated hitting events into executable target-conditioned stroke variations, forming a continuous latent skill space. Second, a high-level planner outputs continuous latent skill codes to compose these skills online according to the observed shuttle state. Third, a context-conditioned adversarial regularizer encourages more natural planner-level skill usage while preserving return performance. When deployed on a real humanoid robot, our system achieves sustained multi-skill rallies with human players, including forehand, backhand, and highly dynamic jump returns. This is the first real-world humanoid racket-sport system to demonstrate multi-skill human--robot rallies including highly dynamic jump returns.

Key Points

  • High-speed racket sports provide a demanding testbed for humanoid robots, requiring time-critical decisions, precise striking, and dynamic whole-body coordination.
  • In badminton, fast-changing shuttle trajectories require timely contact decisions, while successful returns demand precise racket pose and velocity within a brief contact window and across a broad three-dimensional striking workspace.
  • Human motion data provide valuable priors for such athletic skills, but usable badminton references are limited and imperfect.
  • Direct tracking provides insufficient executable variation for diverse shuttle conditions, while purely task-driven optimization may produce unnatural motion.
  • To address these challenges, we present a three-stage hierarchical reinforcement learning framework for dynamic humanoid badminton.

Method

We introduce a three-stage hierarchical framework that constructs a continuous target-conditioned latent skill space through task-randomized motion augmentation, trains a high-level planner for shuttle returns, and regularizes planner-level latent skill usage through a context-conditioned adversarial objective. As illustrated in Fig.

2 , our framework separates skill acquisition, task optimization, and planner-level latent skill regularization.

Method — Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data
Figure 2: Overview of the proposed hierarchical learning framework. We first reconstruct and retarget human badminton videos, annotate hitting events, and perform task-randomized motion augmentation by sampling racket targets for each annotated hitting event, including contact position $p_{\mathrm{target}}$, racket velocity $v_{\mathrm{target}}$, and racket-face direction $d_{\mathrm{target}}$. In (a) , a target-conditioned skill encoder maps reference-motion information, the sampled target, hit timing, and robot context to a latent skill code $z_{t}$, while a low-level controller $\pi_{\phi}$ maps the latent code and robot context to joint actions. In (b) , the low-level controller is frozen, and a high-level planner $\pi_{\theta}$ observes shuttle history and robot state information to output continuous latent codes for shuttle returns. In (c) , a context-conditioned discriminator distinguishes Stage 1-generated latent–context pairs from planner-generated pairs, providing a latent-style reward for regularizing planner-level skill usage.

Experiments

We evaluate the proposed framework in simulation and real-world deployment.

Our experiments address four questions: (1) whether the proposed method improves task performance, stroke diversity, and motion quality over representative baselines; (2) whether task-randomized motion augmentation expands the executable skill space and improves downstream planning; (3) whether staged training facilitates effective task learning and planner-level latent regularization improves motion quality while largely preserving task performance; and (4) whether the...

Experiments — Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data
Figure 3: Left: Randomized shuttle initialization generates diverse incoming trajectories, including high trajectories that create opportunities for jump returns. Right: t-SNE visualization of target-conditioned latent codes collected from Stage 1 rollouts under a fixed reference motion and hitting event. Color indicates the remaining time to hit. Randomized-target codes spread around the corresponding nominal-target codes, forming local neighborhoods of executable stroke variations.

Sources and Demonstrations

Figures are reproduced from Cui et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.

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