Flow-based Vision-Language-Action (VLA) policies are inherently stochastic: different noise seeds can produce action chunks of varying quality from the same observation. At test time, however, the policy typically commits to a single sample without an explicit estimate of its quality. In fine-grained manipulation, exec...
Xue-Li Sun, Yuxiang Lu, Yan-Tong Liu et al.· 0 citations
Recent vision-language models (VLMs) have advanced vision-and-language navigation (VLN), enabling models to predict navigation actions from visual observations and language instructions. In this work, we explore VLN with panoramic observations and introduce PanoVLN. The motivation is straightforward: more complete visu...
Zhen Wang, Chang-Peng Wang, Zhe Liu et al.· 0 citations
This work proposes DreamAvoid, a critical-phase test-time dreaming framework that enables VLA models to anticipate and avoid failures, and introduces an autonomous boundary learning paradigm to refine the system's understanding of the subtle boundary between success and failure.
PlayWorld is introduced, a benchmark providing 171 scenarios, each with a specified objective, to assess models along four core dimensions: geometry consistency, interaction fidelity, out-of-sight evolution, and insight evolution, and basic ability metrics for video quality and controllability.
Kai Ding, Xi Chen, Ming-Hong Cai et al.· 3 citations
StreamPI is proposed, a streaming multimodal temporal modeling framework that equips single-frame VLA with temporal reasoning capability without introducing any additional parameters and seamlessly inherits pretrained single-frame weights and supports flexible single-frame and multi-frame inference.
Zhe Liu, Jing-Hua Hou, Yuxiang Lu et al.· 4 citations· ⚡1
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