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,author={Chen, Qingyi and Kingston, Zachary and Qureshi, Ahmed H.},booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems},note={To Appear},title={{NeHMO}: Neural {Hamilton-Jacobi} Reachability Learning for Decentralized Safe Multi-Agent Motion Planning},year={2026}}
arXiv
Stochastic Neural Signed Swept Volume for Real-time Chance-Constrained Trajectory Optimization
Collision-free motion planning requires reliable collision models from sensed environments and validation of states along a continuous trajectory. To make this tractable, most planners check for collision at discrete states along continuous trajectories against a single determinized model of the environment, introducing a trade-off between safety and computational efficiency. While continuous collision checking approaches that approximate the swept volume of the robot exist, they are computationally expensive or overly conservative. Data-driven approaches can learn the swept volume; however, these neural models are susceptible to approximation errors and are therefore often limited to serving as coarse filters for downstream collision checkers. In this work, we propose to learn a signed distance function of the swept volume as a probabilistic field, enabling quantification of epistemic uncertainty, incorporation of perception noise, and eventual integration into a chance-constrained trajectory optimization framework. We demonstrate our approach on challenging high-dimensional manipulation problems with significant sensor noise, both in simulation and on real hardware.
@misc{chen2026sweptvolume,archiveprefix={arXiv},author={Chen, Qingyi and Zhang, Kevin and Chen, Lucas and Kingston, Zachary},eprint={2609.21211},note={Under Review},primaryclass={cs.RO},title={Stochastic Neural Signed Swept Volume for Real-time Chance-Constrained Trajectory Optimization},year={2026}}
arXiv
Safe Real-Time Policy Steering via Noise-Space Trajectory Optimization for One-Step Generative Policies
Generative robot policies can represent diverse, multimodal behaviors, but adapting pretrained policies to deployment-time constraints such as collision avoidance and orientation maintenance remains challenging. Existing inference-time steering methods typically apply gradient guidance through iterative diffusion or flow processes, which can be computationally expensive for real-time control. We propose INSPO, which formulates inference-time steering of one-step generative policies as trajectory optimization in the policy’s input noise space. By optimizing the input noise while evaluating constraints on the induced state trajectory, INSPO searches the policy-induced behavior space without directly modifying generated actions. The optimization includes a regularization term that encourages solutions to remain consistent with the policy’s input distribution and is solved online using population-based particle optimization. We evaluate INSPO on state- and image-based task-specific policies and generalist vision-language-action policies across Push-T, Can pick-and-place, and LIBERO-Spatial. INSPO improves task success and constraint satisfaction over best-of-N sampling and action projection, while comparing favorably with gradient-guided generation at lower runtime.
@misc{chen2026inspo,archiveprefix={arXiv},author={Chen, Qingyi and Ruan, Joseph and Kingston, Zachary},eprint={2609.21220},note={Under Review},primaryclass={cs.RO},title={Safe Real-Time Policy Steering via Noise-Space Trajectory Optimization for One-Step Generative Policies},year={2026}}
