Author

Hao Zhao

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Preprint Oct 2026

P-BFM: Perceptive Behavioral Foundation Models for Humanoid Control via Unsupervised Reinforcement Learning

Behavioral foundation models (BFMs) provide humanoids with reusable repertoires of whole-body behaviors; in real-world humanoid control, the same behavioral intent may require fundamentally different motions under different terrain constraints. Covering such variation with terrain-matched demonstrations requires enumer...

Xue Wang, Yi-Tang Li, Yi-Ming Zhang et al. · 0 citations

Preprint Oct 2026

SimVTA: Simulation-Grounded Video-Trajectory Alignment for Driving World Action Models

Driving world action models (WAMs) learn from paired trajectories and future observations. Post-training with reinforcement learning allows the policy to explore alternative trajectories and improve planning using driving rewards. However, driving logs contain future videos only for the recorded trajectories, leaving t...

Chang-Hui Jing, Hao-Chen Tian, Hong-Chen Li et al. · 0 citations

Preprint Oct 2026

I-BFM: Reward-Conditioned Robust Humanoid Interaction via Unsupervised Reinforcement Learning

I-BFM is introduced, to the authors' knowledge the first BFM for humanoid-object interaction, rather than relying on task-specific policies or reference tracking, and learns a shared representation of the coupled dynamics among the humanoid, objects, and their contacts using forward-backward representations and unsuper...

Zi-Qi Han, Yi-Tang Li, Jun Sun et al. · 0 citations

Preprint Aug 2026

GaussianWAM: Distilling Geometry and Semantics from 3D Gaussian Fields into World-Action Models

GaussianWAM is proposed, a training-time representation-enhancement framework that organizes geometric and semantic supervision through a 3D Gaussian field and improves performance on standard LIBERO and shows positive transfer trends on RoboTwin and real-world manipulation.

Zi-Jian Zhang, Yu-Qing Jiang, Wei-Tao Zhou et al. · 3 citations