Papers
8
Total Citations
128
H-Index
5
About
Will Shackleford is a leading research engineer at the National Institute of Standards and Technology (NIST), whose career has been defined by advancing the autonomy and safety of mobile robotic systems. His primary research focuses on intelligent control architectures, traversability learning, and performance evaluation for autonomous ground vehicles. Shackleford made a significant impact through his work on the DARPA Learning Applied to Ground Robots (LAGR) program, where he developed hierarchical control systems that integrated machine learning for navigation in complex, unstructured terrain. His most influential paper, "Learning traversability models for autonomous mobile vehicles" (49 citations), established foundational methods for enabling robots to assess and navigate challenging environments. Beyond navigation, Shackleford has pioneered critical work in robotic safety and human-robot interaction, including the development of 3D ground-truth systems for evaluating object and human detection (10 citations) and performance metrics for human tracking in collaborative workspaces. His contributions to smart diagnostics for automated guided vehicles and standardized operator control interfaces have further solidified his reputation as a key figure in creating reliable, measurable, and safe robotic systems for both industrial and defense applications.
Research Focus
Key Achievements
Top Papers
- 1Learning traversability models for autonomous mobile vehicles49 citations · 2007
- 2Learning in a hierarchical control system: 4D/RCS in the DARPA LAGR program39 citations · 2006
- 3Integrating learning into a hierarchical vehicle control system11 citations · 2007
- 43D Ground-Truth Systems for Object/Human Recognition and Tracking10 citations · 2013
- 5
- 6Intelligent Control of Mobility Systems5 citations · 2008
- 7
- 8A common operator control unit color scheme for mobile robots2 citations · 2007