Vision-language-action (VLA) models provide a unified framework for visual understanding, language grounding, and robot control, yet anticipating how the scene will evolve beyond the current observation can provide additional information for control. Action supervision alone does not explicitly encourage such anticipat...
Zi-Le Zhong, Yu-Xin Du, Tao Lin et al.· 0 citations
Pretrained vision-language models (VLMs) already exhibit strong visual, spatial and reasoning capabilities relevant to robotic manipulation. The key challenge in turning pretrained VLMs into generalist manipulation policies is to expose these capabilities through an appropriate action interface. Vision-language-action...
Jing-Kun Yi, Shu-Jie Zhang, Zi-Rui Zhou et al.· 0 citations
MEMOBench is presented, a benchmark for process level memory evaluation in robotic manipulation and provides a diagnostic evaluation suite and training supervision for memory grounded robotic policies.
Hai-Yang Sun, Hao-Xiao Wang, Jun-Ming Chen et al.· 0 citations
PixVL is a self-supervised post-training framework that introduces a unified Mask--Text Consistency Cycle, enabling pixel-level MLLMs to generate and self-verify regional descriptions and learn from unlabeled data.
Yi-Cheng Xiao, Hao-Xuan Ma, Caorui Li et al.· 0 citations
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