Papers
2
Total Citations
8
H-Index
2
About
Chun Wu is a robotics researcher specializing in motion planning for industrial manipulation, with a focus on improving efficiency in repetitive tasks like pick-and-place operations. Their work addresses a critical challenge in automation: how robots can leverage prior experience to accelerate planning in dynamic workspaces. Wu’s major contribution is the development of experience-based planning frameworks that reuse and adapt successful motion plans from previous tasks. Their 2022 paper, “An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place” (6 citations), introduces a graph-based approach that lazily evaluates demonstrations to reduce computation time while maintaining safety in changing environments. Building on this, their 2023 work, “Repetitive Path Planning with Experience-Based Bidirectional RRT” (2 citations), extends bidirectional rapidly-exploring random trees to incorporate past solutions, enabling faster convergence for sequential tasks. Though early in their career, Wu’s research has direct implications for high-mix, low-volume manufacturing and logistics, where robots must adapt quickly without full replanning. Their methods promise to make industrial robots more responsive and energy-efficient, bridging the gap between theoretical motion planning and practical deployment.
Research Focus
Key Achievements
Top Papers
- 1
- 2Repetitive Path Planning with Experience-Based Bidirectional RRT2 citations · 2023