Yuvan Sharma

I am an undergraduate student at UC Berkeley, studying Computer Science and Astrophysics. I work on robotics research at the Berkeley Artificial Intelligence Research (BAIR) Lab, where I am advised by Prof. Trevor Darrell. My research focuses on improving generalization in robotic models through novel architectures and scene representations.

I will be applying to Ph.D. programs this cycle!

Email  /  CV  /  Google Scholar  /  Github

Research

We show it is possible to achieve zero-shot generalization in robotic grasping by training on randomized toy objects and achieving strong performance on real-world objects. This generalization is made possible by a novel object-centric visual representation.

We develop ARM4R, an Autoregressive Robotic Model that leverages low-level 4D Representations learned from human video data. This results in a stronger robotic model with better spatial and temporal understandings.

We introduce RoboPrompt, a framework that enables off-the-shelf text-only LLMs to directly predict robot actions through in-context learning (ICL) without training.

We develop LLARVA, a model trained with a novel instruction tuning method that leverages structured prompts and an auxiliary vision task to unify a range of robotic learning tasks, scenarios, and environments.

Other

Website adapted from Jon Barron.