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The paper applies reinforcement learning with threat-aware reward shaping and adversarial motion priors to teach humanoid robots whole-body evasion of fast projectiles. Its claims rest on simulation and hardware experiments, so readers should check how well the results transfer across obstacle speeds and launch distances, and how much the motion-prior regularization contributes beyond the reward design. For ML readers, it is a useful case study in combining RL with style priors to balance safety, stability, and naturalness in high-dimensional control.
Humanoid Dodgeball: Learning Fast Dynamic Obstacle Avoidance Motions for Humanoids
arXiv:2610.40003v1 Announce Type: new Abstract: Humanoids have become increasingly agile and capable in real-world environments. To operate safely in dynamic scenarios, they must rapidly evade fast-moving obstacles while maintaining stability and physically feasible whole-body motion. Learning such behaviors remains challenging due to the high-dimensional dynamics of humanoid systems and the need to jointly balance reactive obstacle avoidance, motion naturalness, and control robustness. In this work, we present Humanoid Dodgeball, a reinforcement learning (RL) framework for agile dynamic obstacle avoidance in humanoids. Our method enables highly dynamic whole-body evasive maneuvers against incoming projectiles through threat-aware reward shaping, while multiple direction-specific adversarial motion priors regularize the learned behaviors toward stable and diverse human-like motions. Extensive simulation and hardware experiments demonstrate that the proposed framework substantially improves dynamic obstacle avoidance performance across diverse obstacle speeds and launch distances, while preserving robust and physically plausible humanoid motions.
Abstract:Humanoids have become increasingly agile and capable in real-world environments. To operate safely in dynamic scenarios, they must rapidly evade fast-moving obstacles while maintaining stability and physically feasible whole-body motion. Learning such behaviors remains challenging due to the high-dimensional dynamics of humanoid systems and the need to jointly balance reactive obstacle avoidance, motion naturalness, and control robustness. In this work, we present Humanoid Dodgeball, a reinforcement learning (RL) framework for agile dynamic obstacle avoidance in humanoids. Our method enables highly dynamic whole-body evasive maneuvers against incoming projectiles through threat-aware reward shaping, while multiple direction-specific adversarial motion priors regularize the learned behaviors toward stable and diverse human-like motions. Extensive simulation and hardware experiments demonstrate that the proposed framework substantially improves dynamic obstacle avoidance performance across diverse obstacle speeds and launch distances, while preserving robust and physically plausible humanoid motions.
Robotics (cs.RO)
arXiv:2610.40003 [cs.RO]
(or arXiv:2610.40003v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.40003 Focus to learn more arXiv-issued DOI via DataCite
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