Papers
52
Total Citations
4,574
H-Index
24
About
Nathan Ratliff is a pioneering robotics researcher whose work spans motion planning, imitation learning, and autonomous robot behavior. He is perhaps best known as a principal architect of CHOMP (Covariant Hamiltonian Optimization for Motion Planning), a landmark trajectory optimization framework that revolutionized how robots navigate complex environments. Published in both 2009 and 2013, the CHOMP papers have collectively accumulated over 1,700 citations, cementing their status as foundational contributions to the field. Ratliff's earlier work on Maximum Margin Planning (2006, 639 citations) established an influential approach to imitation learning by framing it as a structured prediction problem, enabling robots to learn cost functions directly from expert demonstrations. His research on pedestrian prediction and legged locomotion further demonstrated his ability to apply optimization and learning principles across diverse robotic domains. More recently, his DexPilot system (2020, 197 citations) introduced an accessible, vision-based teleoperation solution for dexterous robotic hands, advancing human-robot collaboration. Across his career, Ratliff has consistently bridged theoretical elegance with practical robotics applications, making his work essential reading for anyone studying robot learning, planning, or manipulation.
Research Focus
Key Achievements
Top Papers
- 1CHOMP: Gradient optimization techniques for efficient motion planning982 citations · 2009
- 2CHOMP: Covariant Hamiltonian optimization for motion planning738 citations · 2013
- 3Maximum margin planning639 citations · 2006
- 4Planning-based prediction for pedestrians469 citations · 2009
- 5Learning to search: Functional gradient techniques for imitation learning206 citations · 2009
- 6DexPilot: Vision-Based Teleoperation of Dexterous Robotic Hand-Arm System197 citations · 2020
- 7Boosting Structured Prediction for Imitation Learning133 citations · 2018
- 8Optimization and learning for rough terrain legged locomotion122 citations · 2011
- 9Real-Time Perception Meets Reactive Motion Generation107 citations · 2018
- 10Imitation learning for locomotion and manipulation105 citations · 2007