Papers
6
Total Citations
39
H-Index
3
About
Shiqi Li is a robotics researcher whose work spans human-robot interaction, autonomous systems, and intelligent task planning. Over more than a decade of contributions, Li has consistently focused on bridging the gap between human cognition and robotic capability, developing systems that allow humans and robots to collaborate more naturally and efficiently. Li's most recognized contribution, "Hybrid Trajectory Replanning-Based Dynamic Obstacle Avoidance for Physical Human-Robot Interaction" (2021, 21 citations), addresses one of robotics' core safety challenges — enabling robots to dynamically avoid obstacles in real time during close physical collaboration with humans. Complementing this, earlier work on semantic object matrices and task-based obstacle avoidance demonstrates Li's interest in context-aware robot decision-making under uncertainty. A particularly distinctive thread in Li's research is the use of natural language to facilitate human-robot cooperation, exploring how spatial cognitive differences between humans and machines can be reconciled through linguistic communication — a forward-thinking direction relevant to modern AI-robotics integration. Li's earlier teleoperation work (2008) laid foundational groundwork in vision-guided autonomous control, while more recent research on hierarchical reinforcement learning tackles complex multi-agent task allocation in challenging environments. Collectively, Li's portfolio reflects a career dedicated to making robots more adaptive, communicative, and genuinely collaborative partners.
Research Focus
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