Stanford AA289/ENGR319 - Robotics and Autonomous Systems Seminar - Stanford Seminar - Representation Learning for Autonomous Robots, Anima Anandkumar

Anima Anandkumar of Caltech and NVIDIA. This talk was given on April 1, 2022. Autonomous robots need to be efficient and agile, and be able to handle a wide range of tasks and environmental conditions. This requires the ability to learn good representations of domains and tasks using a variety of sources such as demonstrations and simulations. Representation learning for robotic tasks needs to be generalizable and robust. I will describe some key ingredients to enable this: (1) robust self-supervised learning (2) uncertainty awareness (3) compositionality. We utilize NVIDIA Isaac for GPU-accelerated robot learning at scale on a variety of tasks and domains. 00:48 Generlizable Learning for Robotics 01:53 Trinity of Generalizable AI 05:08 Physical World if Continuous 07:08 Motivating Problem in Robotics 08:35 State estimation through PDE observer 09:59 Grid-free learning for continuous phenomena 12:32 Neural Operator 15:02 Fourier Transform for global convolution 16:06 FNO: Fourier Neural Operator 18:34 First ML method to solve fluid flow 25:42 Nvidia Modulus 29:08 Operational Space Control (OSC) 38:54 Reducing Supervision and enhancing robustness 47:06 Conclusion Learn more about Stanford Online's Robotics Program and courses here: https://online.stanford.edu/programs/robotics-and-autonomous-systems-graduate-program