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

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arxiv 2502.03206 v3 pith:JRRL77KK submitted 2025-02-05 cs.RO cs.AI

classification cs.ROcs.AI
keywords humanoidlocomotionhugwbcversatilewhole-bodycommandscontrollergeneral
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Scaling Behavior Foundation Model for Humanoid Robots

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A scaling recipe for humanoid behavior foundation models—global-frame motion tracking, on-policy data quantity plus reference diversity, and a transformer with hyperspherical latents—cuts global tracking error by roug...

  2. Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation

    cs.RO 2025-10 conditional novelty 6.0 of 10

    A 10,300-demonstration, 260-task multimodal humanoid manipulation dataset with baseline policy evaluations and a cloud evaluation platform.

  3. A Scalable Whole-body Motion Transfer via Implicit Kinodynamic Motion Retargeting

    cs.RO 2025-09 conditional novelty 6.0 of 10

    A neural retargeting pipeline maps human motion to humanoid robot motion at 5000+ frames per second using a shared latent space and physics-based fine-tuning, filtering noise and producing physically feasible trajectories.

  4. Learning Motion Skills with Adaptive Assistive Curriculum Force in Humanoid Robots

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A2CF uses an adaptive assistive-force agent to guide humanoid robots through training, yielding faster convergence and robust policies that work without the external force.

  5. GMT: General Motion Tracking for Humanoid Whole-Body Control

    cs.RO 2025-06 conditional novelty 6.0 of 10

    GMT trains a single unified humanoid policy using adaptive sampling and mixture-of-experts, achieving lower tracking errors than a re-implemented ExBody2 across diverse whole-body motions.

  6. KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A robot control method that adaptively tightens motion-tracking reward tolerances achieves lower tracking errors on dynamic skills and transfers zero-shot to a real humanoid.

  7. MoRE: Mixture of Residual Experts for Humanoid Lifelike Gaits Learning on Complex Terrains

    cs.RO 2025-06 conditional novelty 6.0 of 10

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  8. A Survey: Learning Embodied Intelligence from Physical Simulators and World Models

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