Conventional flow-based vision-language-action (VLA) models support expressive continuous action generation but rely on multi-step refinement to produce each action chunk, increasing latency in online robot control. To address this issue, we introduce DriftingVLA, a native one-step VLA that generates a complete action...
Yuxuan Gao, Shi-Qi Zhang, Yedong Shen et al.· 2 citations
Fusing tactile signals has proven effective for contact-rich manipulation, enabling robots to perceive contact states and adapt to rapidly changing physical interactions. Yet effectively integrating tactile feedback into dexterous manipulation remains underexplored. In this work, we introduce ReTouch, a vision-language...
Shiqi Zhang, Xin Zhang, Yedong Shen et al.· 1 citation
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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