OTTR: Off-Road Trajectory Tracking using Reinforcement Learning

• URL: http://arxiv.org/abs/2110.02332v1
• Date: Tue, 5 Oct 2021 20:04:37 GMT
• Title: OTTR: Off-Road Trajectory Tracking using Reinforcement Learning
• Authors: Akhil Nagariya, Dileep Kalathil, Srikanth Saripalli
• Abstract summary: We present a novel Reinforcement Learning (RL) algorithm for the off-road trajectory tracking problem. Our approach efficiently exploits the limited real-world data available to adapt the baseline RL policy. Compared to the standard ILQR approach, our proposed approach achieves a 30% and 50% reduction in cross track error in Warthog and Moose.
• Score: 6.033086397437647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we present a novel Reinforcement Learning (RL) algorithm for the off-road trajectory tracking problem. Off-road environments involve varying terrain types and elevations, and it is difficult to model the interaction dynamics of specific off-road vehicles with such a diverse and complex environment. Standard RL policies trained on a simulator will fail to operate in such challenging real-world settings. Instead of using a naive domain randomization approach, we propose an innovative supervised-learning based approach for overcoming the sim-to-real gap problem. Our approach efficiently exploits the limited real-world data available to adapt the baseline RL policy obtained using a simple kinematics simulator. This avoids the need for modeling the diverse and complex interaction of the vehicle with off-road environments. We evaluate the performance of the proposed algorithm using two different off-road vehicles, Warthog and Moose. 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.

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