Uploaded May 2024 | Updated September 2026, 4 hours ago
Maryland Robotics Center Seminar: Learning Hamilton dynamics from video
Christine Allen-Blanchette
Assistant Professor
Department of Mechanical and Aerospace Engineering
Center for Statistics and Machine Learning
Princeton University
In many real-world settings, image observations of physical systems, such as satellites, may be available when low-dimensional measurements are not. However, the high-dimensionality of image data precludes the use of classical estimation techniques to learn the dynamics, and a lack of interpretability reduces the usefulness of standard deep learning methods. In this talk, I will discuss our work on leveraging Lagrangian and Hamiltonian formalisms in neural network design for physically plausible neural network based video prediction and generation. In our prediction pipeline we explicitly construct the equations of motion from learned representations of the underlying physical quantities, and in our generative model we implicitly discover the structure of the configuration space.
For more information on the Maryland Robotics Center see:
https://robotics.umd.edu
Maryland Robotics Center Seminar: Learning Hamilton dynamics from video
Christine Allen-Blanchette
Assistant Professor
Department of Mechanical and Aerospace Engineering
Center for Statistics and Machine Learning
Princeton University
In many real-world settings, image observations of physical systems, such as satellites, may be available when low-dimensional measurements are not. However, the high-dimensionality of image data precludes the use of classical estimation techniques to learn the dynamics, and a lack of interpretability reduces the usefulness of standard deep learning methods. In this talk, I will discuss our work on leveraging Lagrangian and Hamiltonian formalisms in neural network design for physically plausible neural network based video prediction and generation. In our prediction pipeline we explicitly construct the equations of motion from learned representations of the underlying physical quantities, and in our generative model we implicitly discover the structure of the configuration space.
For more information on the Maryland Robotics Center see:
https://robotics.umd.edu
