Yiye Chen

I am a Georgia Tech ECE Ph.D student under the supervision of the Prof. Patricio Vela. I am a member of the Vision-based Manipulation group (VisMan) in the IVALab.

Prior to that, I obtained my B.Eng from the Image Processing Center, Beihang University (BUAA). I did my undergrad thesis under the supervision of Prof. Xiangzhi Bai and Prof. Bindang Xue.

My research interest covers computer vision, machine learning, and their applications in the robotic manipulation.

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Research (* means equal contribution)
WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminative Analysis
Yiye Chen, Yunzhi Lin, Ruinian Xu, Patricio A. Vela
In submission, 2023
paper

KGNv2: Separating Scale and Pose Prediction for Keypoint-based 6-DoF Grasp Synthesis on RGB-D input
Yiye Chen, Ruinian Xu, Yunzhi Lin, Hongyi Chen*, Patricio A. Vela
Submitted to International Conference on Intelligent Robots and Systems (IROS), 2023
paper / video / code

Planning with Language Models through Iterative Energy Minimization
Hongyi Chen*, Yilun Du*, Yiye Chen*, Patricio A. Vela Joshua B. Tenenbaum
International Conference on Learning Representations (ICLR), 2023
paper / project / code

Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input
Yiye Chen, Yunzhi Lin, Ruinian Xu, Patricio A. Vela
IEEE International Conference on Robotics and Automation (ICRA), 2023
paper / video / code

A 6-DoF grasp deteciton approach based on RGB-D input.

A Joint Network for Grasp Detection Conditioned on Natural Language Commands
Yiye Chen, Ruinian Xu, Yunzhi Lin, Patricio A. Vela
IEEE International Conference on Robotics and Automation (ICRA), 2021
paper / video

An end-to-end network that detects grasps affilicated to the target required by a natural language command.

Simultaneous Multi-Level Descriptor Learning and Semantic Segmentation for Domain-Specific Relocalization
Xiaolong Wu*, Yiye Chen*, Cédric Pradalier, Patricio A. Vela
IEEE International Conference on Robotics and Automation (ICRA), 2021
paper

A dense multi-level descriptor learning framework for cross-season localization. We designed a descriptor segmentation module to inject high-level information into the descriptor space from the guidance of semantic labels

Academic Service
  • Conference Reviewer: IROS2023
  • Journal/Letter Reviewer: RA-L