Bingran Chen — accepted AI-conference papers

2025

KGN-Pro: Keypoint-Based Grasp Prediction through Probabilistic 2D-3D Correspondence Learning

Bingran Chen, Baorun Li, Jian Yang, Yong Liu, Guangyao Zhai

IROS 2025

High-level robotic manipulation tasks demand flexible 6-DoF grasp estimation to serve as a basic function. Previous approaches either directly generate grasps from point-cloud data, suffering from challenges with small objects and sensor noise, or infer 3D information from RGB images, which introduc

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2025

L2Calib: SE (3)-Manifold Reinforcement Learning for Robust Extrinsic Calibration with Degenerate Motion Resilience

Baorun Li, Chengrui Zhu, Siyi Du, Bingran Chen, Jie Ren, Wenfei Wang +2

IROS 2025

Extrinsic calibration is essential for multi-sensor fusion, existing methods rely on structured targets or fully-excited data, limiting real-world applicability. Online calibration further suffers from weak excitation, leading to unreliable estimates. To address these limitations, we propose a reinf

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