We present a navigation system that combines ideas from hierarchical planning and machine learning. The system uses a traditional global planner to compute optimal paths towards a goal, and a deep local trajectory planner and velocity controller to compute motion commands. The latter components of the system adjust the behavior of the robot through attention mechanisms such that it moves towards the goal, avoids obstacles, and respects the space of nearby pedestrians. Both the structure of the proposed deep models and the use of attention mechanisms make the system's execution interpretable. Our simulation experiments suggest that the proposed architecture outperforms baselines that try to map global plan information and sensor data directly to velocity commands. In comparison to a hand-designed traditional navigation system, the proposed approach showed more consistent performance.
@article{arxiv.1905.05279,
title = {Deep Local Trajectory Replanning and Control for Robot Navigation},
author = {Ashwini Pokle and Roberto Martín-Martín and Patrick Goebel and Vincent Chow and Hans M. Ewald and Junwei Yang and Zhenkai Wang and Amir Sadeghian and Dorsa Sadigh and Silvio Savarese and Marynel Vázquez},
journal= {arXiv preprint arXiv:1905.05279},
year = {2019}
}