PhysMoDPO: Physically-Plausible Humanoid Motion with Prefere
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Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: PhysMoDPO: Physically-Plausible Humanoid Motion with Preference Optimization. This paper presents PhysMoDPO, a Direct Preference Optimization framework for generating physically-plausible humanoid motion. By integrating a Whole-Body Controller into the training pipeline, the method ensures generat…
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Environment: simulation
“tput of WBC becomes compliant both with physics and original text instructions. To train PhysMoDPO we deploy physics-based and task-specific rewards and use them to assign preference to synthesized trajectories. Our extensive experiments on text-to-motion and spatial control tasks demonstrate consistent improvements of PhysMoDPO in both physical realism and task-related metrics on simulated robots. Moreover, we demon”
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- arXiv Robotics — research abstractsPhysMoDPO: Physically-Plausible Humanoid Motion with Preference Optimization
This paper presents PhysMoDPO, a Direct Preference Optimization framework for generating physically-plausible humanoid motion. By integrating a Whole-Body Controller into the training pipeline, the method ensures generated motions are compliant with both physics and text instructions. Experiments show improvements in physical realism and task performance on simulated and real robots.
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