Ruonan Li

Yiran Ling, Wenxuan Li, Siying Dong et al.

Robot grasping of desktop object is widely used in intelligent manufacturing, logistics, and agriculture.Although vision-language models (VLMs) show strong potential for robotic manipulation, their deployment in low-level grasping faces key challenges: scarce high-quality multimodal demonstrations, spatial hallucination caused by weak geometric grounding, and the fragility of open-loop execution in dynamic environments. To address these challenges, we propose Closed-Loop Asynchronous Spatial Perception(CLASP), a novel asynchronous closed-loop framework that integrates multimodal perception, logical reasoning, and state-reflective feedback. First, we design a Dual-Pathway Hierarchical Perception module that decouples high-level semantic intent from geometric grounding. The design guides the output of the inference model and the definite action tuples, reducing spatial illusions. Second, an Asynchronous Closed-Loop Evaluator is implemented to compare pre- and post-execution states, providing text-based diagnostic feedback to establish a robust error-correction loop and improving the vulnerability of traditional open-loop execution in dynamic environments. Finally, we design a scalable multi-modal data engine that automatically synthesizes high-quality spatial annotations and reasoning templates from real and synthetic scenes without human teleoperation. Extensive experiments demonstrate that our approach significantly outperforms existing baselines, achieving an 87.0% overall success rate. Notably, the proposed framework exhibits remarkable generalization across diverse objects, bridging the sim-to-real gap and providing exceptional robustness in geometrically challenging categories and cluttered scenarios.

Yijing Wang, Ruonan Li, Qilin Wang et al.

3D semantic scene understanding has broad applications in digital twins, autonomous driving, smart agriculture, and embodied perception. However, dense point-wise annotation for point clouds is extremely expensive, making fully supervised 3D semantic learning difficult to scale. Recent annotation-free methods can discover semantic regions without manual 3D labels, but they often suffer from weak object-level consistency, inefficient global grouping, and category-agnostic segmented regions. We propose an annotation-free 3D scene semantic understanding method based on multi-granularity distillation and graph-diffusion-based segmentation. The proposed method first leverages structured visual knowledge guidance and superpoint graph diffusion to perform efficient global semantic propagation, alleviating the problem of inconsistent region-level semantics. It then conducts semantic inference through segmentation-cluster association, assigning interpretable category names to segmented 3D regions and improving the overall effectiveness of annotation-free 3D semantic understanding. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed framework. Compared with the advanced existing annotation-free baselines, our method improves oAcc, mAcc, and mIoU by 5.9%, 8.1%, and 2.4% at most, respectively. These results highlight the promise of the proposed framework for scalable annotation-free 3D scene understanding, especially in real-world scenarios requiring both object segmentation and semantic recognition.