Jixuan Zhi

Jixuan Zhi

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publications

Visibility Integrity Region

Published in IEEE Robotics and Automation Letters, 2019

This paper is about the group following problem in 3D with visibility integrity.

Recommended citation: Jixuan Zhi, Yue Hao, Christopher Vo, Marco Morales, and Jyh-Ming Lien. "Computing 3-d from-region visibility using visibility integrity." IEEE Robotics and Automation Letters 4, no. 4 (2019): 4286-4291. https://ieeexplore.ieee.org/document/8772150/

Shepherding with Deep Reinforcement Learning and Probabilistic Roadmaps in Obstacle-filled environments

Published in IEEE Robotics and Automation Letters, 2021

We propose a deep reinforcement learning (DRL) framework combined with probabilistic roadmaps (PRM) to train a shepherding controller capable of herding agents in obstacle-cluttered environments.

Recommended citation: Jixuan Zhi, and Jyh-Ming Lien. "Learning to herd agents amongst obstacles: Training robust shepherding behaviors using deep reinforcement learning." IEEE Robotics and Automation Letters 6, no. 2 (2021): 4163-4168. https://ieeexplore.ieee.org/document/9387150/

Human-Robot Coexistence Space

Published in IEEE Robotics and Automation Letters, 2021

This paper is about computational design in Human-Robot coexistence space.

Recommended citation: Jixuan Zhi, Lap-Fai Yu, and Jyh-Ming Lien. "Designing human-robot coexistence space." IEEE Robotics and Automation Letters 6, no. 4 (2021): 7161-7168. https://ieeexplore.ieee.org/document/9484808/

Learning to Herd among Obstacles from an Optimized Potential Field with Deep Reinforcement Learning

Published in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022

We propose a deep reinforcement learning (DRL) framework guided by an optimized potential field surrogate to herd large groups (7–8 agents) in obstacle-cluttered environments.

Recommended citation: Jixuan Zhi, and Jyh-Ming Lien. "Learning to Herd Amongst Obstacles from an Optimized Surrogate." In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2954-2961. IEEE, 2022. https://ieeexplore.ieee.org/document/9982269

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