Learning Human-Like Badminton Skills for Humanoid Robots
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908822432382976 |
|---|---|
| author | Chen, Yeke Dong, Shihao Ji, Xiaoyu Sun, Jingkai Luo, Zeren Zhao, Liu Zhang, Jiahui Li, Wanyue Ma, Ji Xu, Bowen Han, Yimin Zhao, Yudong Lu, Peng |
| author_facet | Chen, Yeke Dong, Shihao Ji, Xiaoyu Sun, Jingkai Luo, Zeren Zhao, Liu Zhang, Jiahui Li, Wanyue Ma, Ji Xu, Bowen Han, Yimin Zhao, Yudong Lu, Peng |
| contents | Realizing versatile and human-like performance in high-demand sports like badminton remains a formidable challenge for humanoid robotics. Unlike standard locomotion or static manipulation, this task demands a seamless integration of explosive whole-body coordination and precise, timing-critical interception. While recent advances have achieved lifelike motion mimicry, bridging the gap between kinematic imitation and functional, physics-aware striking without compromising stylistic naturalness is non-trivial. To address this, we propose Imitation-to-Interaction, a progressive reinforcement learning framework designed to evolve a robot from a "mimic" to a capable "striker." Our approach establishes a robust motor prior from human data, distills it into a compact, model-based state representation, and stabilizes dynamics via adversarial priors. Crucially, to overcome the sparsity of expert demonstrations, we introduce a manifold expansion strategy that generalizes discrete strike points into a dense interaction volume. We validate our framework through the mastery of diverse skills, including lifts and drop shots, in simulation. Furthermore, we demonstrate the first zero-shot sim-to-real transfer of anthropomorphic badminton skills to a humanoid robot, successfully replicating the kinetic elegance and functional precision of human athletes in the physical world. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08370 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Human-Like Badminton Skills for Humanoid Robots Chen, Yeke Dong, Shihao Ji, Xiaoyu Sun, Jingkai Luo, Zeren Zhao, Liu Zhang, Jiahui Li, Wanyue Ma, Ji Xu, Bowen Han, Yimin Zhao, Yudong Lu, Peng Robotics Artificial Intelligence Machine Learning Realizing versatile and human-like performance in high-demand sports like badminton remains a formidable challenge for humanoid robotics. Unlike standard locomotion or static manipulation, this task demands a seamless integration of explosive whole-body coordination and precise, timing-critical interception. While recent advances have achieved lifelike motion mimicry, bridging the gap between kinematic imitation and functional, physics-aware striking without compromising stylistic naturalness is non-trivial. To address this, we propose Imitation-to-Interaction, a progressive reinforcement learning framework designed to evolve a robot from a "mimic" to a capable "striker." Our approach establishes a robust motor prior from human data, distills it into a compact, model-based state representation, and stabilizes dynamics via adversarial priors. Crucially, to overcome the sparsity of expert demonstrations, we introduce a manifold expansion strategy that generalizes discrete strike points into a dense interaction volume. We validate our framework through the mastery of diverse skills, including lifts and drop shots, in simulation. Furthermore, we demonstrate the first zero-shot sim-to-real transfer of anthropomorphic badminton skills to a humanoid robot, successfully replicating the kinetic elegance and functional precision of human athletes in the physical world. |
| title | Learning Human-Like Badminton Skills for Humanoid Robots |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2602.08370 |