Papers
5
Total Citations
51
H-Index
3
About
Min Wan is an emerging researcher specializing in robotic manufacturing, machining dynamics, and intelligent motion planning, with a growing body of work that bridges advanced control theory and practical automation challenges. His most influential contribution, "Profile Error-Oriented Optimization of the Feed Direction and Posture of the End-Effector in Robotic Free-Form Milling" (2023, 32 citations), demonstrates his ability to address real-world precision challenges in industrial robotics by optimizing end-effector behavior to minimize surface errors. Building on this foundation, Wan has made significant strides in understanding chatter stability in robotic milling, investigating the complex influences of low-frequency vibrations and structural nonlinearities on machining performance. His innovative application of frequency-domain decomposition and stability lobe diagram prediction reflects a sophisticated grasp of nonlinear dynamics. More recently, Wan has expanded into machine learning-driven approaches, employing Gaussian process regression and proper orthogonal decomposition to enable rapid, pose-dependent dynamic predictions—reducing computational overhead substantially. His newest work on diffusion trajectory-guided policies for long-horizon robot manipulation signals an exciting pivot toward embodied AI and imitation learning, marking him as a versatile researcher whose contributions span precision manufacturing, dynamic modeling, and next-generation robotic intelligence.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5Diffusion Trajectory-Guided Policy for Long-Horizon Robot Manipulation2 citations · 2025