Pranav Atreya

Hello! I’m a first year PhD student in CS at UC Berkeley, where I am advised by Prof. Sergey Levine. I am graciously supported in part by the NSF Graduate Research Fellowship.

I recently graduated with my BS in Computer Science at UT Austin, where I was a member of the Turing Scholars and Dean’s Scholars Honors programs. At UT I was fortunate to be advised by Prof. Joydeep Biswas, Prof. Eunsol Choi, and Prof. Yuke Zhu.

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Crafting In-context Examples according to LMs' Parametric Knowledge
Yoonsang Lee*, Pranav Atreya*, Xi Ye, Eunsol Choi
Preprint, 2023

What prompts elicit language models to best answer knowledge intensive questions? We find that a mix of in-context examples that the model knows how to answer and doesn't know yields optimal QA performance.

Zero-Shot Robotic Manipulation with Pre-Trained Image-Editing Diffusion Models
Kevin Black*, Mitsuhiko Nakamoto*, Pranav Atreya, Homer Walke, Chelsea Finn,
Aviral Kumar, Sergey Levine
Preprint, 2023
arXiv / website

SuSIE leverages the internet pretraining of image generation models like InstructPix2Pix to achieve zero-shot robot manipulation on unseen objects, distractors, and scenes.

High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization
Pranav Atreya, Haresh Karnan, Kavan Singh Sikand, Xuesu Xiao,
Sadegh Rabiee, Joydeep Biswas
IROS, 2022
arXiv / video

We devise Optim-FKD, a new paradigm for accurate, high speed control of a robot using a learned forward kinodynamic model and non-linear least squares optimization.

VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics
Haresh Karnan, Kavan Singh Sikand, Pranav Atreya, Sadegh Rabiee,
Xuesu Xiao, Garrett Warnell, Peter Stone, Joydeep Biswas
IROS, 2022
arXiv / video

In this work we learn a visual inverse kinodynamic model conditioned on image patches of the terrain ahead to better enable high-speed navigation on multiple different terrains.

State Supervised Steering Function for Sampling-based Kinodynamic Planning
Pranav Atreya, Joydeep Biswas
AAMAS, 2022
arXiv / video

Optimal sampling-based motion planning algorithms when applied to kinodynamic planning make a trade off between computational efficiency and solution quality. With S3F we demonstrate that both are attainable by proposing a new way to learn the steering function required by these sampling-based planners.

Website adapted from Jon Barron