Yuankai Lu
Papers
2
Total Citations
5
H-Index
2
About
Yuankai Lu is a rising researcher in the field of robotic manipulation and intelligent manufacturing, with a focus on force-controlled end-effectors and adaptive trajectory learning. His work addresses critical challenges in automating precision tasks such as polishing and obstacle avoidance, where traditional robotic tools often fall short due to size constraints or lack of user adaptability. In his 2024 study on a force-controlled end-effector using pneumatic artificial muscle, Lu introduced a compact, compliant design that enables robots to perform polishing inside deep holes and grooves—a common bottleneck in industrial automation. This work has already garnered 3 citations, signaling early impact in the robotics community. Complementing this, Lu developed a Kernelized Movement Primitives (KMP)-based interactive learning approach that allows robots to adapt trajectories in real time while avoiding obstacles, with user input guiding the adaptation process. This method, earning 2 citations, empowers non-expert users to teach robots complex maneuvers intuitively. Together, Lu’s contributions advance the frontier of human-robot collaboration and adaptive automation, positioning him as a promising innovator in next-generation robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2