A Sim-to-Real Pipeline for Deep Reinforcement Learning for Autonomous Robot Navigation in Cluttered Rough Terrain

Robots that autonomously navigate real-world 3D cluttered environments need to safely traverse terrain with abrupt changes in surface normals and elevations. In this letter, we present the development of a novel sim-to-real pipeline for a mobile robot to effectively learn how to navigate real-world 3D rough terrain environments. The pipeline uses a deep reinforcement learning architecture to learn a navigation policy from training data obtained from the simulated environment and a unique combination of strategies to directly address the reality gap for such environments. Experiments in the real-world 3D cluttered environment verified that the robot successfully performed point-to-point navigation from arbitrary start and goal locations while traversing rough terrain. A comparison study between our DRL method, classical, and deep learning-based approaches showed that our method performed better in terms of success rate, and cumulative travel distance and time in a 3D rough terrain environment.

Automated summary

The study presented a novel sim-to-real pipeline for a mobile robot to effectively learn how to navigate real-world 3D rough terrain environments, with experiments showing that our method outperformed classical and deep learning-based approaches in terms of success rate, cumulative travel distance, and time in a 3D rough terrain environment.