arXiv (All)
open access
Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data
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 a
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Editorial: Multidisciplinary aspects and performance in racket sports-volume III.#433803
- Stated, Realized, and Optimal Aiming Strategies for the Tennis Serve: Experimental Evidence from Collegiate Athletes#1155276
- Fine reconstruction of badminton swing dynamic trajectory assisted by event camera.#209700
- An Ensemble of Long Short-Term Memory Models to Automatically Detect End-Range Movement Patterns in Men's Professional Hard Court Grand Slam Tennis.#157123
- A Physiological and Notational Comparison of the Conventional and New Scoring Systems in Badminton#1026219
Record · ID 1115796
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive