aiqbench analysis
This research introduces a real-time framework using autoregressive diffusion models to decouple locomotion velocity from stylistic expression in legged robots. By leveraging unstructured motion data and reinforcement learning, the system achieves robust control over physics-based characters without requiring explicit style labels during training. The approach represents a significant advancement in expressive robotics, demonstrating how generative AI can enhance dynamic movement generation for physical agents.
Walk Like This: Stylized Locomotion for Physics-Based Characters through Example-Driven Diffusion
arXiv:2610.18147v1 Announce Type: new Abstract: We present a real-time framework for expressive legged locomotion that enables independent control over velocity and style. Unlike existing data-driven controllers, which typically require explicit style labels for training, our method efficiently leverages large-scale unstructured motion data and generalizes to unseen styles. We disentangle style from functional locomotion through a tailored sampling strategy used to train a style encoder whose output conditions an autoregressive diffusion model. During inference, the user provides a style reference clip and a joystick velocity command. The diffusion model then generates a kinematic reference tracked by a reinforcement learning controller. To improve user controllability and stylization strength, we propose an attention biasing mechanism and a velocity command correction strategy. We demonstrate our method on a physics-based humanoid character in simulation and on a physical robot, showcasing robust real-time control and continuous style modulation. The results can be best seen in our supplemental video (https://www.youtube.com/watch?v=roFnS6N7nJ4).
Abstract:We present a real-time framework for expressive legged locomotion that enables independent control over velocity and style. Unlike existing data-driven controllers, which typically require explicit style labels for training, our method efficiently leverages large-scale unstructured motion data and generalizes to unseen styles. We disentangle style from functional locomotion through a tailored sampling strategy used to train a style encoder whose output conditions an autoregressive diffusion model. During inference, the user provides a style reference clip and a joystick velocity command. The diffusion model then generates a kinematic reference tracked by a reinforcement learning controller. To improve user controllability and stylization strength, we propose an attention biasing mechanism and a velocity command correction strategy. We demonstrate our method on a physics-based humanoid character in simulation and on a physical robot, showcasing robust real-time control and continuous style modulation. The results can be best seen in our supplemental video (this https URL).
Robotics (cs.RO)
arXiv:2610.18147 [cs.RO]
(or arXiv:2610.18147v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.18147 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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