Work

LiCS: Navigation Using Learned-Imitation on Cluttered Space

Year: 2024

Type: article

Abstract: This work proposes a robust and fast navigation system in a narrow indoor environment for UGV (Unmanned Ground Vehicle) using 2D LiDAR. We used behavior cloning with Transformer neural network to learn the optimization-based baseline algorithm. We inject Gaussian noise during expert demonstration to increase the robustness of the learned policy and evaluate the performance of LiCS using both simulation and hardware experiments. It outperforms all other baselines in terms of navigation performance, achieving a success rate 100% in highly cluttered simulated environments. During the hardware experiments, LiCS can maintain safe navigation at maximum speed of$\text{1.5 m/s}$.

Source: IEEE Robotics and Automation Letters

DOI: 10.1109/lra.2024.3518078

Authors: Joshua Julian Damanik, Jae-Won Jung, Chala Adane Deresa, Han-Lim Choi

Institution: Korea Advanced Institute of Science and Technology

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Chung Mong-Koo Foundation

Award: G4081632356