Papers
19
Total Citations
498
H-Index
9
About
Yuxiao Chen is a robotics and autonomous systems researcher whose work spans safety-critical control, multi-robot coordination, motion planning, and human-robot interaction. He is perhaps best known for his foundational contributions to Control Barrier Functions (CBFs), particularly his highly cited 2020 work on guaranteed obstacle avoidance for multi-robot systems (164 citations), which introduced a decentralized supervisory framework that ensures safe operation even under limited actuation constraints. Building on this foundation, Chen extended CBF methodology into data-driven settings through Koopman operator theory, enabling safety guarantees for systems where dynamics must be learned rather than modeled analytically. Beyond safety, Chen has made significant contributions to autonomous vehicle planning, developing branch model predictive control for multimodal interactive scenarios (72 citations) and bi-level imitation learning for realistic traffic simulation (54 citations). His decentralized task and path planning framework for multi-robot systems (59 citations) further demonstrates his breadth across collaborative robotics. Chen's research also touches on assistive robotics, including robot-assisted feeding and bipedal walking stabilization, reflecting a commitment to real-world deployment across diverse domains. With over 450 cumulative citations, his work has established him as an influential voice in making autonomous systems both provably safe and practically capable.
Research Focus
Key Achievements
Top Papers
- 1
- 2Interactive Multi-Modal Motion Planning With Branch Model Predictive Control72 citations · 2022
- 3Decentralized Task and Path Planning for Multi-Robot Systems59 citations · 2021
- 4BITS: Bi-level Imitation for Traffic Simulation54 citations · 2023
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- 7Balancing Efficiency and Comfort in Robot-Assisted Bite Transfer16 citations · 2022
- 8Collaborative Welding and Joint Sealing Robots With Haptic Feedback12 citations · 2021
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