I am passionate about Robotics, Control theory, Computer Vision and Reinforcement Learning, and my goal is to empower the multi-robot system with intelligence(robust and interactive autonomy), which enables robots to make informed decisions for safely and effectively collaborating with each other and with humans in the physical world. Currently, I am conducting simulations and real-world verifications of multi-agent control systems, focusing on developing algorithms for large-scale mixed-autonomy coordination.
π₯ News
[Jan. 2026] Our paper about geometry-aware CBF is accepted by ICRA 2026.
[Jun. 2025] I will present our Multi-robot Deadlock Resolution paper in MMLS 2025.
[Jan. 2025] Our paper about Multi-robot Deadlock Resolution is accepted to ICRA' 25.
[Jan. 2025] Our paper about Sample-efficient Safe Reinforcement Learning is accepted to ICRA' 25.
We present a novel decentralized framework that ensures both efficient task execution and deadlock
resolution for multi-agent systems, which enables co-optimization
between the task-related controller and deadlock resolution
controller, yielding smoother robotsβ motion with improved overall task execution efficiency
**Courteous MPC for Autonomous Driving with CBF-inspired Risk Assessment**
Yanze Zhang, Yiwei Lyu, Sude E. Demir, Xingyu Zhou, Yupeng Yang, Junming Wang, Wenhao Luo
IEEE International Conference on Intelligent Transportation Systems (ITSC), 2024
we develop an extension of the CBF-inspired risk evaluation framework that takes into account both noisy observed positions and motions, which are then integrated with MPC to ensure smooth and robust decision-making for AVs.
All Publications
Adaptive Deadlock Avoidance for Decentralized Multi-Agent Systems via CBF-inspired Risk Measurement.
Yanze Zhang, Yiwei Lyu, Siwon Jo, Yupeng Yang, and Wenhao Luo
Accepted to the 2025 IEEE International Conference on Robotics and Automation (ICRA' 25), 2025