Papers
2
Total Citations
83
H-Index
2
About
Kunpeng Li is a multidisciplinary researcher whose work spans intelligent automation, rehabilitation robotics, and operations optimization. His research sits at a compelling intersection of artificial intelligence and real-world engineering challenges, with notable contributions to both warehouse logistics and clinical rehabilitation technology. In the domain of intelligent logistics, Li has pioneered the application of reinforcement learning to complex operational problems, most prominently through his development of a hyper-heuristic framework for autonomous guided vehicle (AGV) task assignment and route planning in parts-to-picker warehouse environments — a paper that has already garnered 56 citations since its 2024 publication, reflecting its immediate relevance to the rapidly evolving field of warehouse automation. Li's research extends meaningfully into healthcare technology, where he has contributed rigorous systematic review and meta-analytic evidence on robot-assisted gait training for subacute stroke patients, helping clinicians better understand how emerging rehabilitation technologies compare to conventional approaches. This work, with 27 citations, underscores his commitment to evidence-based evaluation of novel interventions. Collectively, Li's scholarship demonstrates a rare breadth — bridging industrial AI optimization and medical robotics — making him a valuable voice across engineering, logistics, and neurorehabilitation research communities.
Research Focus
Key Achievements
Top Papers
- 1
- 2