arXiv Machine Learning
One Policy, Many Styles: Where to Aggregate Preferences for Steerable Humanoid Control?
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Critic Architecture Matters: Dual vs. Unified Critics for Humanoid Loco-Manipulation
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PAWS: Preference Learning with Advantage-Weighted Segments
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From Digital Human Interactions to Physics-Based Humanoid Skills: Physics-Grounded Post-Training of Interaction Generators
The paper introduces DIGHT, a co‑adaptive framework that couples a Digital Human Interaction Generator with a Humanoid Tracking policy. DIGHT first runs multiple text‑conditioned interaction candidates in simulation, then builds physics‑grounded preferences from the rollouts—covering executability and interaction fidelity—and uses diffusion‑based direct preference optimization to align the generator without back‑propagating through the simulator. The aligned generator supplies reference motions for fine‑tuning the tracker, leading to more physically plausible and faithful humanoid interactions in simulation.