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.

> 05/2023 — Co-organized the 12th F1TENTH Racing Competition
> 10/2022 — Presented at IROS in Kyoto, Japan!
> 07/2022 — Two papers accepted at IROS
> 05/2022 — Presented paper at AAMAS
> 03/2022 — Winner of CTURC undergraduate research competition
> 12/2021 — Paper accepted at AAMAS

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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