Real-world Vision-Language Navigation (VLN) requires agents to act under partial observability, where map structure, semantic cues, and target locations may be incomplete or incorrectly inferred by foundation models. This paper presents a lightweight, training-free, and uncertainty-aware framework for cooperative multi...
Fan Pu, Keshu Wu, Ji-Wan Jiang et al.· 0 citations
Experiments show that current VLA policies struggle with zero-shot personalization, while SFT makes preferences learnable, and a controlled formulation and taxonomy of observable preferences in simulation is proposed, covering motion, skill, and user-profile choices.
Zhi-Xu Li, Ke Tang, Li-Tian Gong et al.· 0 citations
How should demonstration quality be used during vision-language-action (VLA) pretraining to improve performance after target-task fine-tuning? We compare weighting, filtering, and conditioning across reward definitions and label granularities using 5,000 naturally suboptimal human demonstrations and 24 target tasks. Wi...
Ming-Xuan Yan, Li-Tian Gong, Di Huang et al.· 0 citations
AXIS, a growable community-driven data engine and benchmark for scalable robot learning, is presented, which enables browser-based teleoperation for large-scale demonstration collection, automatically generates and validates new manipulation tasks, and transforms community-collected demonstrations into training-ready d...
