English

A Unified and General Humanoid Whole-Body Controller for Versatile Locomotion

Robotics 2025-04-15 v3 Artificial Intelligence

Abstract

Locomotion is a fundamental skill for humanoid robots. However, most existing works make locomotion a single, tedious, unextendable, and unconstrained movement. This limits the kinematic capabilities of humanoid robots. In contrast, humans possess versatile athletic abilities-running, jumping, hopping, and finely adjusting gait parameters such as frequency and foot height. In this paper, we investigate solutions to bring such versatility into humanoid locomotion and thereby propose HugWBC: a unified and general humanoid whole-body controller for versatile locomotion. By designing a general command space in the aspect of tasks and behaviors, along with advanced techniques like symmetrical loss and intervention training for learning a whole-body humanoid controlling policy in simulation, HugWBC enables real-world humanoid robots to produce various natural gaits, including walking, jumping, standing, and hopping, with customizable parameters such as frequency, foot swing height, further combined with different body height, waist rotation, and body pitch. Beyond locomotion, HugWBC also supports real-time interventions from external upper-body controllers like teleoperation, enabling loco-manipulation with precision under any locomotive behavior. Extensive experiments validate the high tracking accuracy and robustness of HugWBC with/without upper-body intervention for all commands, and we further provide an in-depth analysis of how the various commands affect humanoid movement and offer insights into the relationships between these commands. To our knowledge, HugWBC is the first humanoid whole-body controller that supports such versatile locomotion behaviors with high robustness and flexibility.

Keywords

Cite

@article{arxiv.2502.03206,
  title  = {A Unified and General Humanoid Whole-Body Controller for Versatile Locomotion},
  author = {Yufei Xue and Wentao Dong and Minghuan Liu and Weinan Zhang and Jiangmiao Pang},
  journal= {arXiv preprint arXiv:2502.03206},
  year   = {2025}
}

Comments

Published at RSS 2025. The first two authors contribute equally. Project page: https://hugwbc.github.io/