no code implementations • 5 Mar 2024 • Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, Sujay Sanghavi, Sandeep Chinchali
Current approaches to time series generation often ignore this paired metadata, and its heterogeneity poses several practical challenges in adapting existing conditional generation approaches from the image, audio, and video domains to the time series domain.
no code implementations • 20 Sep 2023 • Junette Hsin, Shubhankar Agarwal, Adam Thorpe, Luis Sentis, David Fridovich-Keil
To overcome this, we combine Gaussian process regression with a sparse identification of nonlinear dynamics (SINDy) method to denoise the data and identify nonlinear dynamical equations.
no code implementations • 7 Mar 2023 • Oguzhan Akcin, Po-han Li, Shubhankar Agarwal, Sandeep Chinchali
Instead, we propose a cooperative data sampling strategy where geo-distributed AVs collaborate to collect a diverse ML training dataset in the cloud.
no code implementations • 22 Sep 2022 • Shubhankar Agarwal, David Fridovich-Keil, Sandeep P. Chinchali
In order to model real-world factors affecting future forecasts, we introduce the notion of an adversary, which perturbs observed historical time series to increase a robot's ultimate control cost.
no code implementations • 31 Jul 2020 • Shubhankar Agarwal, Harshit Sikchi, Cole Gulino, Eric Wilkinson, Shivam Gautam
A popular way to plan trajectories in dynamic urban scenarios for Autonomous Vehicles is to rely on explicitly specified and hand crafted cost functions, coupled with random sampling in the trajectory space to find the minimum cost trajectory.