activity
20242026
most citedLearning Granularity-Aware Affordances from Human-Object Interaction for Tool-Based Functional Dexterous Grasping

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

BLaFA: Bridging Language to 3D Functional Affordances for Dexterous Robotic Grasping in Tool Use

Fan Yang, Wenrui Chen, Guorun Yan +7

In industrial assembly, equipment maintenance, and everyday tool-use scenarios, robots need not only to grasp objects stably but also to establish task-appropriate contact configur…

cs.RO2026

TRACER: Texture-Robust Affordance Chain-of-Thought for Deformable-Object Region Grounding

Wanjun Jia, Kang Li, Fan Yang +7

The central challenge in robotic manipulation of deformable objects lies in aligning high-level semantic instructions with physical interaction points under complex appearance and…

cs.CV2026

Learning Fine-Grained Correspondence with Cross-Perspective Perception for Open-Vocabulary 6D Object Pose Estimation

Yu Qin, Shimeng Fan, Fan Yang +5

Open-vocabulary 6D object pose estimation empowers robots to manipulate arbitrary unseen objects guided solely by natural language. However, a critical limitation of existing appro…

cs.CV2025

NRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World Models

Siyu Li, Fei Teng, Yihong Cao +3

Birds' Eye View (BEV) semantic segmentation is an indispensable perception task in end-to-end autonomous driving systems. Unsupervised and semi-supervised learning for BEV tasks, a…

cs.CV2025

One-Shot Affordance Grounding of Deformable Objects in Egocentric Organizing Scenes

Wanjun Jia, Fan Yang, Mengfei Duan +6

Deformable object manipulation in robotics presents significant challenges due to uncertainties in component properties, diverse configurations, visual interference, and ambiguous…

cs.RO2025

Multi-Keypoint Affordance Representation for Functional Dexterous Grasping

Fan Yang, Dongsheng Luo, Wenrui Chen +5

Functional dexterous grasping requires precise hand-object interaction, going beyond simple gripping. Existing affordance-based methods primarily predict coarse interaction regions…

cs.RO2024★ 1 cited

Learning Granularity-Aware Affordances from Human-Object Interaction for Tool-Based Functional Dexterous Grasping

Fan Yang, Wenrui Chen, Kailun Yang +5

To enable robots to use tools, the initial step is teaching robots to employ dexterous gestures for touching specific areas precisely where tasks are performed. Affordance features…