Weisheng Xu
Publications
Rule-VLN: Bridging Perception and Compliance via Semantic Reasoning and Geometric Rectification
As embodied AI transitions to real-world deployment, the success of the Vision-and-Language Navigation (VLN) task tends to evolve from mere reachability to social compliance. However, current agents suffer from a "goal-driven trap", prioritizing physical geometry ("can I go?") over semantic rules ("may I go?"), frequently overlooking subtle regulatory constraints. To bridge this gap, we establish Rule-VLN, the first large-scale urban benchmark for rule-compliant navigation. Spanning a massive 29k-node environment, it injects 177 diverse regulatory categories into 8k constrained nodes across four curriculum levels, challenging agents with fine-grained visual and behavioral constraints. We further propose the Semantic Navigation Rectification Module (SNRM), a universal, zero-shot module designed to equip pre-trained agents with safety awareness. SNRM integrates a coarse-to-fine visual perception VLM framework with an epistemic mental map for dynamic detour planning. Experiments demonstrate that while Rule-VLN challenges state-of-the-art models, SNRM significantly restores navigation capabilities, reducing CVR by 19.26% and boosting TC by 5.97%.
UniGround: Universal 3D Visual Grounding via Training-Free Scene Parsing
3D Visual Grounding (3DVG) localizes objects from natural-language descriptions in 3D scenes and is fundamental to embodied AI applications. Although foundation models enable open-vocabulary reasoning, they typically rely on pre-generated candidates, creating two sequential bottlenecks. The \emph{candidate bottleneck} occurs when dataset-specific 3D proposal models miss, fragment, or incorrectly group targets under distribution shifts, excluding them from VLM reasoning. The \emph{evidence bottleneck} stems from incomplete visual evidence: global renderings preserve spatial context but obscure object details, whereas candidate-centric views capture local appearance but lack global context. To address these bottlenecks, we propose UniGround, a zero-shot 3DVG framework that addresses both bottlenecks through Global Candidate Filtering and Contextual Precision Grounding. Global Candidate Filtering constructs topology-consistent, class-agnostic candidates from 3D topology and multi-view semantic cues, without dataset-trained 3D detectors, task-specific proposal supervision, or predefined box and category priors. Contextual Precision Grounding jointly reasons over global spatial context and candidate-centric visual evidence, followed by closed-loop consistency verification for reliable target identification. UniGround achieves 46.1\%/34.1\% Acc@0.25/0.5 on ScanRefer and 28.7\% Acc@0.25 on the evaluated ARKitScenes subset of EmbodiedScan. Further experiments demonstrate competitive grounding without dataset-specific 3D priors, cross-dataset generalization to unseen indoor scenes, and robustness to real-world reconstruction noise and practical domain shifts.