Ajay Sridhar

I am an undergraduate at UC Berkeley, majoring Electrical Engineering and Computer Science (EECS) and minoring in Logic. My current research is with Prof. Sergey Levine at the Robotic AI and Learning Lab. I am interested in building generalizable and robust robot learning systems that continuously improve with experience. Previously, I worked with Prof. Thomas Dietterich on domain generalization techniques in computer vision.

I have been a teaching assistant for CS 188: Introduction to Artificial Intelligence for the past four semesters under Prof. Stuart Russell, Prof. Dawn Song, Dr. Igor Mordatch, and Peyrin Kao.

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SELFI is an online reinforcement learning approach for fine-tuning control policies trained with model-based learning. We combine the objective used during model-based learning with a Q-value function learned online.

NoMaD is a novel architecture for robotic navigation in previously unseen environments that uses a unified diffusion policy to jointly represent exploratory task-agnostic behavior and goal-directed task-specific behavior.

ViNT is a flexible Transformer-based model for visual navigation that can be efficiently adapated to a variety of downstream navigational tasks.

SACSoN is vision-based navigation policy that learns socially unobtrusive behavior in human-occupied spaces through continual learning.

GNM is vision-based navigation policy trained with a simple goal-reaching objective on a cross-embodiment navigation dataset. It exhibits positive transfer, outperforming specialist models trained on singular embodiment datasets, and generalizes to new robots.

ExAug is a vision-based navigation policy that learns to control robots with varying camera types, camera placements, robot sizes, and velocity constraints by applying a novel geometric-aware objective to view augmented data.

Source code from Jon Barron's website.