Humanoid World Action Model With Joint State--Action Generation
Humanoid World Action Model With Joint State--Action Generation
This research proposes HWAM, a Humanoid World Action Model that integrates state-action generation to bridge the gap between policy references and executed motion in humanoid robots. The model is trained through three conditional paths and outperforms existing baselines in real-robot tasks, achieving a 70.6% success rate in Candy Picking.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
Reported success rate
70.6%
What remains unknown
Not established in the collected evidence: Environment, Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
erences, realized body motion, and visual outcomes. HWAM achieves the highest success rate among evaluated baselines on three real-robot tasks on the LimX OLI humanoid. On Candy Picking, HWAM achieves a 70.6% success rate, compared with 43.3% for Fast-WAM.
Open source S6
Source：arXiv Robotics — research abstracts · arxiv.org