RoboStriker: Hierarchical Decision-Making for Autonomous Humanoid Boxing

• URL: http://arxiv.org/abs/2601.22517v1
• Date: Fri, 30 Jan 2026 03:51:58 GMT
• Title: RoboStriker: Hierarchical Decision-Making for Autonomous Humanoid Boxing
• Abstract summary: RoboStriker is a hierarchical three-stage framework that enables fully autonomous humanoid boxing.<n>The framework first learns a comprehensive repertoire of boxing skills by training a single-agent motion tracker on human motion capture data.<n>In the final stage, we introduce Latent-Space Neural Fictitious Self-Play (LS-NFSP), where competing agents learn competitive tactics by interacting within the latent action space.
• Score: 43.240817529539356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Achieving human-level competitive intelligence and physical agility in humanoid robots remains a major challenge, particularly in contact-rich and highly dynamic tasks such as boxing. While Multi-Agent Reinforcement Learning (MARL) offers a principled framework for strategic interaction, its direct application to humanoid control is hindered by high-dimensional contact dynamics and the absence of strong physical motion priors. We propose RoboStriker, a hierarchical three-stage framework that enables fully autonomous humanoid boxing by decoupling high-level strategic reasoning from low-level physical execution. The framework first learns a comprehensive repertoire of boxing skills by training a single-agent motion tracker on human motion capture data. These skills are subsequently distilled into a structured latent manifold, regularized by projecting the Gaussian-parameterized distribution onto a unit hypersphere. This topological constraint effectively confines exploration to the subspace of physically plausible motions. In the final stage, we introduce Latent-Space Neural Fictitious Self-Play (LS-NFSP), where competing agents learn competitive tactics by interacting within the latent action space rather than the raw motor space, significantly stabilizing multi-agent training. Experimental results demonstrate that RoboStriker achieves superior competitive performance in simulation and exhibits sim-to-real transfer. Our website is available at RoboStriker.

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