Carolina Chang
Papers
8
Total Citations
114
H-Index
6
About
Carolina Chang is a robotics researcher whose work spans the intersecting domains of biologically inspired robotics, autonomous navigation, search and rescue systems, and aerial robotics. She is perhaps best known for her pioneering application of biological learning principles to robot behavior, most notably her development of neural network models based on operant conditioning that enable mobile robots to learn approach and avoidance behaviors without supervision — work that has garnered over 37 citations and remains a foundational contribution to adaptive robotics. Her early models of self-organizing neural controllers for obstacle avoidance further cemented her reputation in biomimetic robot control. Chang has also made meaningful contributions to humanitarian robotics, exploring vision-based person detection using template matching and advocating for robot-assisted mass-casualty triage in emergency response scenarios. Her 2009 work on real-time video stabilization for small robotic helicopters demonstrates a versatile research portfolio extending into aerial platforms. Across her career, Chang has been a thoughtful voice in defining the boundaries and goals of biorobotics as a discipline, helping to clarify its identity within both the robotics and biology communities.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Real-time video smoothing for small RC helicopters16 citations · 2009
- 4A Model of Operant Conditioning for Adaptive Obstacle Avoidance12 citations · 1996
- 5Towards Robot-Assisted Mass-Casualty Triage11 citations · 2007
- 6Biomimetic Robotics11 citations · 2000
- 7An Approach to Vision-Based Person Detection in Robotic Applications5 citations · 2005
- 8Biorobotics researcher: To be or not to be?3 citations · 2001