Dynamic object manipulation requires robots to anticipate target motion while replanning fast enough to avoid executing outdated actions. Existing approaches either rely on fast reactive control without an explicit future target state or encode anticipated motion only implicitly. World Action Models (WAMs) offer a prom...
Hao-Han Geng, Sha Zhang, Wei Sun et al.· 1 citation
World Action Models (WAMs) unify visual world modeling and action generation, yet their real-world deployment is heavily bottlenecked by iterative action denoising and the sequential execution of visual prefilling and action prediction. We demonstrate that the action-denoising trajectories in WAMs are approximately str...
Run-Dong Su, Pengtao Chen, Hao-Han Geng et al.· 0 citations
Wet-lab experimentation serves as the gold standard for hypothesis verification in scientific discovery; yet it is inherently labor-intensive, costly, and safety-critical. Embodied agents hold the promise of automating these tedious workflows, but their development is hindered by the scarcity of real-world training dat...
Chen-Xi Li, Hai-Yuan Wan, Rui Li et al.· 0 citations
ReTouch is introduced, a vision-language-action model (VLA) that supports contact-rich dexterous manipulation through tactile predictions continually refined online using execution-time feedback, and keeps tactile predictions aligned with evolving physical interactions, enabling responsive action correction and improvi...
Shi-Qi Zhang, Xin Zhang, Yedong Shen et al.· 4 citations
This work introduces ThinkAfford, which decouples high-recall affordance proposal generation from instruction-grounded reasoning and outperforming comparable 3D open-vocabulary and vision-language-model-based 2D-to-3D baselines.
Xinrui Lin, Sha Zhang, Shu-Min Wang et al.· 0 citations
RoMAN-Flow (Robotic Manipulation with Autoregressive Normalizing Flows), an offline reinforcement learning framework that makes AR-NF policies practical for robotic manipulation by addressing this sampling bottleneck in both stages.
Shao-Xuan Wang, Guangting Zheng, Rui Huang et al.· 0 citations
The experimental results validate the effectiveness of the LabDex task design and demonstration data, and show that the benchmark supports the training and systematic evaluation of existing robotic policies across laboratory dexterous manipulation tasks at different levels, providing a foundation for further research a...
Zhipeng Tang, Sihan Chen, Sha Zhang et al.· 0 citations
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