Author

Hao Zhao

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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

Behavioral foundation models (BFMs) have recently shown that a single humanoid policy can support diverse whole-body control, but extending such generality to physical interaction remains challenging. We introduce I-BFM, to our knowledge the first BFM for humanoid-object interaction. Rather than relying on task-specifi...

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