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Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data — arXiv:2609.31840

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.31840
License (as archived): CC0-1.0
Archived: 2026-10-08T00:16:49.380Z
SHA-256 of the archived text: 1fbdc50566c5a0ee151c58ad95fb251e9560e2f7fec29a0b17e1efbd37dd8625
Stable URL of this page: https://metraipolis.com/documento/dc_fbebea2ba9ff

How to cite

Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data — arXiv:2609.31840 — arXiv (en), https://arxiv.org/abs/2609.31840 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fbebea2ba9ff (Metraipolis archive dc_fbebea2ba9ff, sha256 1fbdc50566c5a0ee151c58ad95fb251e9560e2f7fec29a0b17e1efbd37dd8625)

BibTeX

@misc{dcfbebea2ba9ff,
  title = {Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data — arXiv:2609.31840},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.31840},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fbebea2ba9ff; sha256 1fbdc50566c5a0ee151c58ad95fb251e9560e2f7fec29a0b17e1efbd37dd8625},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.31840. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-29 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.31840 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T23:52:16.378Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).