Jason Stanley
Papers
2
Total Citations
27
H-Index
2
About
Jason Stanley is a leading researcher at the intersection of robotics, machine learning, and geometric mechanics, with a core focus on learning physically-consistent dynamics models for robot control. His major contributions lie in developing data-driven approaches that embed the fundamental structure of Hamiltonian and port-Hamiltonian systems directly into neural network architectures. By leveraging Lie group theory, Stanley’s work ensures that learned models respect the underlying geometry and energy conservation laws of robotic systems, enabling more accurate, stable, and generalizable control. His 2024 paper on "Port-Hamiltonian Neural ODE Networks on Lie Groups" has already garnered 22 citations, highlighting its immediate impact on the field. In a complementary 2024 work, he pioneered methods for learning Hamiltonian dynamics directly from raw point cloud observations, addressing the challenge of adapting control policies to changing operational conditions for nonholonomic mobile robots. Stanley’s research is notable for bridging the gap between classical mechanics and modern deep learning, offering a principled path toward robots that can autonomously adapt to novel environments while maintaining safety and stability guarantees.
Research Focus
Key Achievements
Top Papers
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- 2