I am a first year PhD student at CoMMA lab, also joint advised by professor Lin Tan. My research interests span several areas across Deep Learning and Artificial Intelligence, but I hold a central curiosity for how intelligent systems can begin to replicate human adaptability in different environments. In particular, I am drawn to the fields of robotics and natural language processing. Both fields can be unified by a need for quick adaptation of general knowledge models, and I hope to develop scalable and efficient learning-based methods to address these limitations in real-world contexts. My long-term goal is to contribute to model distillation, few-shot, and novel methods in these fields that empower intelligent systems to adapt to new environments or tasks with limited resources.
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}}
