Noah Duncan
Papers
1
Total Citations
27
H-Index
1
About
Noah Duncan’s research lies at the intersection of computer vision and robotics, with a particular focus on reasoning about object affordances—the actionable properties of objects. His most-cited work, “Fill and Transfer: A Simple Physics-Based Approach for Containability Reasoning” (2015, 27 citations), introduces a novel method for understanding whether a container can hold liquid. By combining geometric analysis with physics-based simulations, Duncan’s approach enables robots to infer affordances like containability from visual data alone, a critical step toward more intuitive human-robot interaction. This work stands out for its simplicity and effectiveness, offering a practical framework for tasks such as pouring, transferring, and manipulating everyday objects. Duncan’s contributions have helped bridge the gap between low-level perception and high-level reasoning, providing a foundation for autonomous systems that can interact with their environment in more human-like ways. His research continues to influence how robots perceive and act upon the world, making him a notable figure in the growing field of affordance-based robotics.
Research Focus
Key Achievements
Top Papers
- 1