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Ken Chen

7 accepted papers

2024

Enhancing Closed-Loop Performance in Learning-Based Vehicle Motion Planning by Integrating Rule-Based Insights

RA-L 2024

This letter introduces an innovative vehicle motion planning method that leverages the integration of rule-based insights to significantly improve closed-loop performance within a learning-based framework. We first employ rule-based methods to heuristically search and generate a diverse set of traje

Cited by 2SourceScholar
2024

RoScenes: A Large-scale Multi-view 3D Dataset for Roadside Perception

ECCV 2024poster

"We introduce RoScenes, the largest multi-view roadside perception dataset, which aims to shed light on the development of vision-centric Bird’s Eye View (BEV) approaches for more challenging traffic scenes. The highlights of RoScenes include significantly large perception area, full scene coverage…

2022

Model-driven reinforcement learning and action dimension extension method for efficient asymmetric assembly

ICRA 2022poster

Complex assembly tasks remain huge challenge for robots because the traditional control methods rely on complicated contact state analysis. Reinforcement learning (RL) becomes one of the preferred embodiments to construct the control strategy of complex tasks. In this paper, the method of model-driv…

Cited by 5SourceScholar
2020

A Compliance Control Method Based on Viscoelastic Model for Position-Controlled Humanoid Robots

IROS 2020poster

Compliance is important for humanoid robots, especially a position-controlled one, to perform tasks in complicated environments where unexpected or sudden contacts will result in large impacts which may cause instability or destroy the hardware of robots. This paper presents a compliance control met…

Cited by 9SourceScholar
2020

Rotation Consistent Margin Loss for Efficient Low-Bit Face Recognition

CVPR 2020poster

In this paper, we consider the low-bit quantization problem of face recognition (FR) under the open-set protocol. Different from well explored low-bit quantization on closed-set image classification task, the open-set task is more sensitive to quantization errors (QEs). We redefine the QEs in angula…

Cited by 50PDFScholar