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20232026
most citedManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation

3 citations · 13 across the 22 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.RO2025

Real2Edit2Real: Generating Robotic Demonstrations via a 3D Control Interface

Yujie Zhao, Hongwei Fan, Di Chen +5

Recent progress in robot learning has been driven by large-scale datasets and powerful visuomotor policy architectures, yet policy robustness remains limited by the substantial cos…

cs.RO2025

Imagine2Act: Leveraging Object-Action Motion Consistency from Imagined Goals for Robotic Manipulation

Liang Heng, Jiadong Xu, Yiwen Wang +6

Relational object rearrangement (ROR) tasks (e.g., insert flower to vase) require a robot to manipulate objects with precise semantic and geometric reasoning. Existing approaches e…

cs.RO2025

SR3D: Unleashing Single-view 3D Reconstruction for Transparent and Specular Object Grasping

Mingxu Zhang, Xiaoqi Li, Jiahui Xu +5

Recent advancements in 3D robotic manipulation have improved grasping of everyday objects, but transparent and specular materials remain challenging due to depth sensing limitation…

cs.RO2025

BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly

Yan Shen, Ruihai Wu, Yubin Ke +6

Shape assembly, the process of combining parts into a complete whole, is a crucial robotic skill with broad real-world applications. Among various assembly tasks, geometric assembl…

cs.RO2025

CrayonRobo: Object-Centric Prompt-Driven Vision-Language-Action Model for Robotic Manipulation

Xiaoqi Li, Lingyun Xu, Mingxu Zhang +8

In robotic, task goals can be conveyed through various modalities, such as language, goal images, and goal videos. However, natural language can be ambiguous, while images or video…

cs.CV2025

3DWG: 3D Weakly Supervised Visual Grounding via Category and Instance-Level Alignment

Xiaoqi Li, Jiaming Liu, Nuowei Han +4

The 3D weakly-supervised visual grounding task aims to localize oriented 3D boxes in point clouds based on natural language descriptions without requiring annotations to guide mode…