Latent generative models typically follow a two-stage pipeline, training a variational autoencoder for reconstruction and then a generative model on the frozen latent space. Since reconstruction-optimized latents are not necessarily generation-friendly, jointly training both models is an appealing alternative. However,...
Guangting Zheng, Yiyuan Zhang, Tao Yang et al.· 2 citations· ⚡1
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...
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
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