Download PDFOpen PDF in browserUsing Simulation to Design Path Following and Obstacle Avoidance Policies for Autonomous RobotsEasyChair Preprint 132962 pages•Date: May 16, 2024AbstractThis study explores the development of neural network-based control policies for autonomous robots, focusing on path following and obstacle avoidance. Utilizing the Autonomy Research Testbed (ART) and the Chrono simulation engine, we crafted two control strategies: an end-to-end imitation learning policy and a hybrid policy combining path following with a value function-based obstacle controller. Preliminary simulations validate both approaches, highlighting their respective efficiencies in managing complex navigation tasks. Future efforts will address transferring these policies to real vehicles, emphasizing the reduction of the sim-to-real performance gap. Keyphrases: Autonomy Policy Design, Autonomy Simulator, Sim2Real
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