S. Thornton
Papers
2
Total Citations
39
H-Index
2
About
S. Thornton is a pioneering researcher in cognitive robotics, with a primary focus on developing robots that can learn and reason about affordance relations—the actionable possibilities between an agent and its environment. Their major contribution lies in introducing a novel framework that enables robots to learn general affordance relations through internal rehearsal, a process that mimics human cognitive simulation. In their seminal 2008 paper, "Towards a cognitive robot that uses internal rehearsal to learn affordance relations" (31 citations), Thornton proposed a two-component approach: modeling affordances as statistical relations among actions, object properties, and outcomes, then using internal simulation to predict and refine these relations without physical interaction. This work was further developed in "A robot rehearses internally and learns an affordance relation" (8 citations), which demonstrated practical implementation. Thornton’s research bridges cognitive science and robotics, offering a scalable path toward autonomous learning. Their work is foundational for students and researchers interested in developmental robotics, machine learning, and embodied cognition, providing a framework that reduces the need for extensive real-world training while enhancing robot adaptability.
Research Focus
Key Achievements
Top Papers
- 1
- 2A robot rehearses internally and learns an affordance relation8 citations · 2008