Jarvis Schultz
Papers
8
Total Citations
108
H-Index
6
About
Jarvis Schultz is a roboticist whose research lies at the intersection of nonlinear control, trajectory optimization, and autonomous systems. His work focuses on developing algorithms that enable robots to perform complex, dynamic tasks with limited actuation and sensing. Schultz is perhaps best known for pioneering the use of Sequential Action Control (SAC) for real-time trajectory generation, as demonstrated in his highly cited work on information-maximizing control for the Baxter robot (17 citations). He has also made significant contributions to underactuated systems, including trajectory generation for a magnetically-suspended differential drive robot (26 citations), and to the engineering of autonomous theatrical performances with robotic puppets (17 citations). His research on trajectory optimization for well-conditioned parameter estimation (21 citations) has advanced the field of experimental design for dynamical systems. More recently, Schultz has developed efficient computational methods for higher-order variational integrators in robotic simulation and trajectory optimization (7 citations). With over 100 total citations, his work bridges theory and practice, offering robust solutions for high-dimensional and hybrid robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Trajectory generation for underactuated control of a suspended mass26 citations · 2012
- 2Trajectory Optimization for Well-Conditioned Parameter Estimation21 citations · 2014
- 3
- 4Robotic Puppets and the Engineering of Autonomous Theater17 citations · 2014
- 5Model-Based Reactive Control for Hybrid and High-Dimensional Robotic Systems12 citations · 2016
- 6
- 7
- 8Autonomous Visual Rendering using Physical Motion2 citations · 2020