Papers
1
Total Citations
62
H-Index
1
About
Shanqi Yang is a leading researcher in human-robot collaboration (HRC), with a primary focus on integrating deep reinforcement learning to enhance safety and productivity in industrial settings. His most cited work, "Towards Safe Human-Robot Collaboration Using Deep Reinforcement Learning" (2020, 62 citations), addresses a critical bottleneck in HRC: the over-emphasis on safety measures that stifle efficiency. Yang’s major contribution lies in developing adaptive, data-driven frameworks that replace traditional, costly risk assessment processes for layout reconfigurations. By treating robots as dynamic hazards, his models enable real-time decision-making, reducing downtime while maintaining rigorous safety standards. This work has garnered attention for its potential to transform manufacturing floors, where reconfigurability is key. Yang’s research bridges the gap between theoretical reinforcement learning and practical industrial applications, offering a roadmap for safer, more agile automation. His achievements include advancing the field’s understanding of how robots can learn to collaborate without compromising human well-being, a challenge that has long hindered widespread HRC adoption. For students and researchers, Yang’s work exemplifies how AI can solve real-world safety dilemmas, making him a pivotal figure in the evolution of collaborative robotics.
Research Focus
Key Achievements
Top Papers
- 1Towards Safe Human-Robot Collaboration Using Deep Reinforcement Learning62 citations · 2020