Jeremy R. Cooperstock

About

Jeremy R. Cooperstock is a researcher whose work has made meaningful contributions to the field of robotics, particularly in the area of vision-guided robotic control and machine learning-based robot navigation. His research has focused on developing intelligent systems that enable robots to perform complex tasks — such as docking and target reaching — without relying on traditional geometric calibration methods, instead allowing robots to learn through visual observation and experience. Cooperstock's most notable contributions center on self-supervised and adaptive neural network architectures applied to robotic arms and autonomous systems. His 1993 work on self-supervised learning for docking and target reaching, which has garnered 15 citations, laid early groundwork in this space. Subsequent papers from 2002 and 2003 further refined these ideas, demonstrating that neural networks trained through visual feedback could effectively guide robotic systems in real-world conditions with minimal prior configuration. What distinguishes Cooperstock's approach is its emphasis on practical, efficiently trainable systems — making sophisticated robotic control more accessible and adaptable. His body of work reflects a consistent commitment to bridging machine learning theory with tangible robotics applications, offering valuable insights for students and researchers working at the intersection of computer vision, neural networks, and autonomous robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Self-supervised learning for docking and target reaching

   15 citations · 1993
2. 2
   An efficiently trainable neural network based vision-guided robot arm

   7 citations · 2002
3. 3
   Adaptive neural networks for vision-guided position control of a robot arm

   6 citations · 2003

Key Collaborators

Contact & Links