OTTR: Off-Road Trajectory Tracking using Reinforcement Learning

no code implementations • 5 Oct 2021 • Akhil Nagariya, Dileep Kalathil, Srikanth Saripalli

Compared to the standard ILQR approach, our proposed approach achieves a 30% and 50% reduction in cross track error in Warthog and Moose, respectively, by utilizing only 30 minutes of real-world driving data.

reinforcement-learning Reinforcement Learning (RL)

An Iterative LQR Controller for Off-Road and On-Road Vehicles using a Neural Network Dynamics Model

no code implementations • 28 Jul 2020 • Akhil Nagariya, Srikanth Saripalli

We use model predictive control (MPC) to deal with model imperfections and perform extensive experiments to evaluate the performance of the controller on human driven reference trajectories with vehicle speeds of 3m/s- 4m/s for warthog and 7m/s-10m/s for the Polaris GEM

Model Predictive Control