Toward Agile Maneuvers in Highly Constrained Spaces: Learning From Hallucination

While classical approaches to autonomous robot navigation currently enable operation in certain environments, they break down in tightly constrained spaces, e.g., where the robot needs to engage in agile maneuvers to squeeze between obstacles. Recent machine learning techniques have the potential to...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 6; no. 2; pp. 1503 - 1510
Main Authors: Xiao, Xuesu, Liu, Bo, Warnell, Garrett, Stone, Peter
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Autonomous navigation
Autonomous vehicle navigation
Barriers
Collision avoidance
Hallucinations
imitation learning
Machine learning
machine learning for robot control
Maneuvers
motion and path planning
Navigation
Planning
Robot sensing systems
Robots
sensorimotor learning
Training
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:While classical approaches to autonomous robot navigation currently enable operation in certain environments, they break down in tightly constrained spaces, e.g., where the robot needs to engage in agile maneuvers to squeeze between obstacles. Recent machine learning techniques have the potential to address this shortcoming, but existing approaches require vast amounts of navigation experience for training, during which the robot must operate in close proximity to obstacles and risk collision. In this letter, we propose to side-step this requirement by introducing a new machine learning paradigm for autonomous navigation called learning from hallucination (LfH), which can use training data collected in completely safe environments to compute navigation controllers that result in fast, smooth, and safe navigation in highly constrained environments. Our experimental results show that the proposed LfH system outperforms three autonomous navigation baselines on a real robot and generalizes well to unseen environments, including those based on both classical and machine learning techniques.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3058927