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Learning robotic navigation from experience: principles, methods and recent results.

Sergey Levine¹, Dhruv Shah¹

¹Berkeley AI Research (BAIR), UC Berkeley, 2121 Berkeley Way, Berkeley, CA 94704, USA.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences

|December 13, 2022

Summary

Robotic navigation is moving beyond geometry using machine learning for better real-world decision-making. Experiential learning enables robots to improve navigation skills through accumulated data and experience.

Keywords:

learning machine learning navigation reinforcement learning robotics

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Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Traditional robotic navigation relies on geometric mapping and planning.
Real-world navigation involves complex physical challenges beyond simple geometric models.
Machine learning offers a novel approach to enhance robotic decision-making.

Purpose of the Study:

To present a unified toolkit for experiential learning in robotic navigation.
To detail the design principles behind this approach.
To summarize experimental findings and discuss future research directions.

Main Methods:

Developing a general toolkit for experiential learning in robotics.
Unifying recent machine learning approaches for navigation.
Leveraging prior experience and real-world data for decision-making.

Main Results:

Demonstrated ability of machine learning systems to reason about traversability beyond geometry.
Systems that account for physical outcomes and exploit environmental patterns.
Potential for continuous improvement and network effects with more data.

Conclusions:

Experiential learning provides a powerful framework for advancing robotic navigation.
Machine learning enables robots to navigate complex environments more effectively.
This approach promises more adaptable and intelligent autonomous systems.