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: