Before joining Purdue, I completed my B.S. at Shanghai Jiao Tong University and the University of Michigan. I later earned my M.S. at the University of Michigan, where I worked in the ROAHM Lab under the supervision of Professor Ram Vasudevan.
My research focuses on safe (multi-)robot motion planning and, more broadly, on combining model-based methods with learning-enabled components so that robots can carry out a range of tasks such as manipulation safely and efficiently.
Safe multi-arm motion planning is a challenging problem in robotics due to its high dimensionality, coupled configuration space, and complex collision constraints. Centralized planners are capable of coordinating all arms but often face scalability limitations, restricting applicability in real-time settings. On the other hand, decentralized methods are scalable and recent deep learning-based approaches have shown promising results. However, these depend on accurate behavior prediction or coordination protocols and may fail when other arms act unpredictably. To address these challenges, we introduce a neural Hamilton-Jacobi Reachability (HJR) learning-based approach to approximate a safety value function that captures worst-case inter-arm safety constraints. We further develop a decentralized trajectory optimization framework that uses the learned HJR representation for real-time planning. The proposed method is scalable and data-efficient, generalizes across multi-manipulator systems, and outperforms state-of-the-art baselines on challenging multi-arm motion planning tasks.
@inproceedings{chen2026nehmo,title={{NeHMO}: Neural {Hamilton-Jacobi} Reachability Learning for Decentralized Safe Multi-Agent Motion Planning},author={Chen, Qingyi and Kingston, Zachary and Qureshi, Ahmed H.},booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems},year={2026},note={To Appear},}
