Zeqing Zhang
Papers
2
Total Citations
9
H-Index
2
About
Zeqing Zhang is a robotics researcher focused on advancing autonomous navigation and manipulation in complex, unstructured environments. Their work bridges the gap between theoretical motion planning and real-world robotic control, with key contributions in reinforcement learning for fluid dynamics and accelerated collision-free navigation. Zhang’s most cited paper, "GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning" (2023, 7 citations), pioneers the formulation of robotic water scooping as a goal-conditioned reinforcement learning problem. This work tackles the formidable challenge of fluid dynamics and multi-modal goal achievement, enabling a policy to simultaneously satisfy position and orientation objectives—a significant leap for robotic manipulation in dynamic settings. Additionally, in "RDA: An Accelerated Collision Free Motion Planner for Autonomous Navigation in Cluttered Environments" (2022, 2 citations), Zhang addresses the computational bottleneck of nonconvex collision avoidance constraints by introducing a planner that exploits constraint structures, drastically reducing computation time for safe navigation. These contributions demonstrate Zhang’s impact in making robots more adaptive and efficient in real-world scenarios, from scooping liquids to navigating tight spaces, earning recognition for practical, scalable solutions in robotics.
Research Focus
Key Achievements
Top Papers
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- 2