DiffWBC: Unlocking Steerable General Humanoid Motion Tracking via One-Step Diffusion Whole-Body Control
Overview
This paper introduces DiffWBC, a generative whole-body control framework for humanoid robots that unifies general motion tracking, test-time steering, and simulation-free failure discovery. The method utilizes a one-step diffusion model to achieve low local tracking error (20.1 mm on BONES) and zero-shot adaptation to various tasks including hurdle navigation and actuator faults without retraining or physics simulations.
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Reported quantity · tracking error: 20.1 other · Basis not reported
“mulation-free failure discovery within a single controller: (1) Generative general tracking: DiffWBC requires a single forward pass per 50 Hz control step and can be distilled from an RL teacher or trained from scratch via model-based RL, achieving the lowest local tracking error among six general trackers on held-out benchmarks (20.1 mm on BONES) and deploying on a physical Unitree G1. (2) Zero-shot test-time steeri”
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Reported quantity · Reported precision: 91 percent · Basis not reported
“s (91% precision in top 2%) without physics simulations; targeted fine-tuning on flagged clips resolves 50% of failure cases. Videos and interactive demonstrations are available at https://diffwbc.github.io/#selfassess.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalDiffWBC: Unlocking Steerable General Humanoid Motion Tracking via One-Step Diffusion Whole-Body Control
This paper introduces DiffWBC, a generative whole-body control framework for humanoid robots that unifies general motion tracking, test-time steering, and simulation-free failure discovery. The method utilizes a one-step diffusion model to achieve low local tracking error (20.1 mm on BONES) and zero-shot adaptation to various tasks including hurdle navigation and actuator faults without retraining or physics simulations.
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