7 papers
From Routes to Steps: Separating Semantic Progress from Local Execution in Vision-and-Language Navigation
Xiangyun Huang, Xiangchen Wang, Runfeng Lin +5
Vision-and-Language Navigation (VLN) requires an agent to follow a route-level instruction by executing its constituent steps from egocentric visual observations. Existing VLM-base…
: Toward Versatile Embodied Agents
Shengli Zhou, Xiangchen Wang, Jinrui Zhang +5
Embodied agents have demonstrated promising capabilities in interacting with physical environments. Yet, versatile embodied agents face three core bottlenecks: dynamic environmenta…
CAPruner: Conceptual-Adjacent Scene Graph Pruner for Enhancing 3D Spatial Reasoning of Large Language Models
Shengli Zhou, Xiangchen Wang, Guanhua Chen +1
Large language models (LLMs) have recently been applied to 3D vision-language (3D-VL) tasks, which require spatial reasoning to identify target objects relative to anchors. Scene g…
LiveVLN: Breaking the Stop-and-Go Loop in Vision-Language Navigation
Xiangchen Wang, Weiye Zhu, Teng Wang +5
Recent navigation systems achieve strong benchmark results, yet real-world deployment often remains visibly stop-and-go. This bottleneck arises because the sense-inference-executio…
\textsc{NaVIDA}: Vision-Language Navigation with Inverse Dynamics Augmentation
Weiye Zhu, Zekai Zhang, Xiangchen Wang +5
Vision-and-Language Navigation (VLN) requires agents to interpret natural language instructions and act coherently in visually rich environments. However, most existing methods rel…
ActiveVLN: Towards Active Exploration via Multi-Turn RL in Vision-and-Language Navigation
Zekai Zhang, Weiye Zhu, Hewei Pan +4
The Vision-and-Language Navigation (VLN) task requires an agent to follow natural language instructions and navigate through complex environments. Existing MLLM-based VLN methods p…