Yong–Lu Li

Shanghai Jiao Tong University, China XD Group (China)

Papers

Total Citations

H-Index

About

Yong-Lu Li is a rising researcher at the forefront of embodied AI and human-robot interaction, with key contributions spanning human trajectory prediction, robot manipulation skill acquisition, and real-time facial expression recognition. His most impactful work, "Human Trajectory Prediction with Momentary Observation" (2022, 30 citations), addresses a critical challenge in autonomous systems—predicting future human movements from limited, real-world observations—a vital capability for self-driving cars and social robots. Li also pioneers human-agent joint learning frameworks (2025, 4 citations) that leverage teleoperation systems to efficiently teach robots complex manipulation skills, bridging the gap between human demonstration and robotic dexterity. His work on EC-RFERNet (2023, 4 citations) advances edge computing for real-time facial expression recognition, enabling low-latency, on-device AI. In his latest work, "Motion Before Action" (2025, 3 citations), Li introduces a novel paradigm that reasons about object motion from visual observations before generating robot action sequences, significantly enhancing imitation learning performance. With a growing citation impact and a focus on making robots more perceptive, adaptive, and collaborative, Yong-Lu Li is shaping the future of autonomous systems that seamlessly integrate with human environments.