RoboStriker: 自律型ヒューマノイドボクシングのための潜在空間戦略ゲーム
RoboStriker: Latent-Space Strategic Games for Autonomous Humanoid Boxing
ヒューマノイドボクシングを2プレイヤーの潜在空間ゼロサムマルコフゲームとして定式化し、戦略的探索と物理的実現性の矛盾を解決する階層的フレームワークを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Kangning Yin, Kaige Liu, Zhe Cao, Wentao Dong, Weishuai Zeng, Tianyi Zhang, Qiang Zhang, Jingbo Wang, Jiangmiao Pang, Yang Li, Ming Zhou, Weinan Zhang
分類: cs.RO
原文アブストラクト
Achieving human-level competitive intelligence and physical agility in humanoid robots remains a profound challenge, particularly in contact-rich and highly dynamic tasks such as boxing. While Multi-Agent Reinforcement Learning offers a principled framework for strategic interaction, its direct application to unstructured raw motor spaces inevitably leads to joint-level physical collapse, preventing the emergence of any viable combat tactics. To resolve this fundamental conflict between strategic exploration and physical feasibility, we formulate the humanoid combat task as a novel two-player latent-space zero-sum Markov game. Under standard regularity and approximate best-response assumptions, we show that the latent formulation induces an equivalent game over the decoder-reachable action manifold, providing an approximate-Nash interpretation of the resulting self-play dynamics. To instantiate this theoretical formulation, we propose RoboStriker, a hierarchical framework that decouples high-level reasoning from low-level execution. It first distills the tracking expertise of predefined boxing motions into a topologically bounded latent manifold. This structured latent foundation subsequently drives multi-agent co-evolution via Latent-Space Neural Fictitious Self-Play. Extensive experimental results demonstrate that gaming within this structured latent space substantially outperforms direct exploration. By constraining strategic exploration through a pretrained motion decoder, RoboStriker substantially reduces the catastrophic balance failures observed in raw action-space methods and achieves superior tactical performance in both competitive win rates and striking efficiency. Finally, we successfully deploy and validate our learned combat policies on real-world humanoid robots. Our code and video and supplementary materials are available at RoboStriker.
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