Zhuoqun Chen

University at Buffalo, State University of New York

Papers

Total Citations

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About

Zhuoqun Chen is a rising researcher at the forefront of robotics and autonomous systems, with a primary focus on kinematics-aware trajectory planning and vision-to-planning integration. His most notable contribution, the paper "iKap: Kinematics-Aware Planning with Imperative Learning" (2025), introduces a novel framework that bridges the gap between high-level visual perception and low-level robot control. By embedding kinematic constraints directly into an imperative learning paradigm, Chen’s work enables robots to generate collision-free, executable pose sequences that are both efficient and adaptive to dynamic environments. This approach addresses a critical bottleneck in traditional modular systems, which often suffer from disjointed perception and planning pipelines. Although early in his career, his work has already garnered attention, with 2 citations for this seminal paper. Chen’s research holds significant promise for advancing real-world robotic applications, from autonomous navigation to manipulation, by ensuring that planned trajectories are not only theoretically sound but practically reliable. His innovative fusion of learning and planning marks him as a key contributor to the next generation of intelligent, adaptive robotics.