BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion
Qiayuan Liao, Takara E. Truong, Xiaoyu Huang, Yuman Gao, Guy Tevet, Koushil Sreenath, C. Karen Liu

TL;DR
BeyondMimic introduces a versatile humanoid control framework using guided diffusion, enabling seamless composition of diverse agile motions and solving unseen tasks with high naturalness and zero-shot transfer to real hardware.
Contribution
It presents a unified diffusion-based model for scalable, natural, and versatile humanoid motion synthesis and task generalization, surpassing prior motion-specific approaches.
Findings
Achieves state-of-the-art human-like agility including flips and sprinting.
Enables zero-shot transfer of learned skills to real hardware.
Supports diverse downstream tasks like obstacle avoidance and teleoperation.
Abstract
The human-like form of humanoid robots positions them uniquely to achieve the agility and versatility in motor skills that humans possess. Learning from human demonstrations offers a scalable approach to acquiring these capabilities. However, prior works either produce unnatural motions or rely on motion-specific tuning to achieve satisfactory naturalness. Furthermore, these methods are often motion- or goal-specific, lacking the versatility to compose diverse skills, especially when solving unseen tasks. We present BeyondMimic, a framework that scales to diverse motions and carries the versatility to compose them seamlessly in tackling unseen downstream tasks. At heart, a compact motion-tracking formulation enables mastering a wide range of radically agile behaviors, including aerial cartwheels, spin-kicks, flip-kicks, and sprinting, with a single setup and shared hyperparameters, all…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRobot Manipulation and Learning · Human Motion and Animation · Robotic Locomotion and Control
