Papers
37
Total Citations
891
H-Index
16
About
James Law is a robotics and human-robot interaction researcher whose work spans developmental robotics, collaborative manufacturing, and the social dynamics of robot deployment. Drawing inspiration from infant cognitive development, his early research established biologically grounded frameworks for autonomous robotic learning, including sensorimotor integration for eye-head gaze control and active vision, work that has collectively attracted tens of thousands of reads and laid important theoretical groundwork in developmental robotics. As the field evolved toward industrial applications, Law pivoted to human-robot collaboration in Industry 4.0 and 5.0 contexts, contributing a ROS-integrated API for the KUKA LBR iiwa and pioneering modular Digital Twin frameworks for safety assurance in collaborative robotics. His most-cited paper (144 citations), published in 2023, demonstrates how deep learning-enhanced Digital Twins can meaningfully improve safety and reliability in smart manufacturing environments. Beyond technical systems, Law has made notable contributions to understanding the human side of robotics, investigating how trust is shaped by context and individual factors, how social-cognitive recovery strategies influence robot likability, and how intuitive graphical signage can reduce user anxiety. This rare combination of cognitive, engineering, and psychological perspectives makes Law's body of work particularly valuable for researchers designing robots that must work safely and effectively alongside people.
Research Focus
Key Achievements
Top Papers
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- 5A ROS-integrated API for the KUKA LBR iiwa collaborative robot43 citations · 2017
- 6The infant development timeline and its application to robot shaping42 citations · 2011
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