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Improving Autonomous Robotic Navigation Using Imitation Learning.

Brian Cèsar-Tondreau1,2, Garrett Warnell2, Ethan Stump2

  • 1Unmanned Systems Laboratory, Mechanical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States.

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Summary
This summary is machine-generated.

This study introduces a Machine Learning module trained with human demonstrations to intuitively adapt robot navigation policies. The approach simplifies customization for non-technical users, improving navigation success and trajectory similarity.

Keywords:
autonomous navigationhuman in the loopimitation learninglearning from demonstrationrobot learning and behavior adaptation

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Autonomous navigation relies on complex, expert-modified path planning modules.
  • Customizing navigation behavior requires significant technical expertise.

Purpose of the Study:

  • To simplify the modification of robot navigation policies for non-technical users.
  • To enable intuitive adaptation of navigation behavior using human demonstrations.

Main Methods:

  • Inserted a Machine Learning module into a standard navigation stack.
  • Trained the module using Behavioral Cloning with human demonstration data.
  • Integrated the ML module between global path planning and local motion planning.

Main Results:

  • The approach successfully adapted robot navigation behavior to match human demonstrators.
  • Achieved favorable navigation success rates and trajectory similarity compared to baselines.
  • Demonstrated effective policy modification with limited demonstration data.

Conclusions:

  • Machine Learning, specifically Behavioral Cloning, offers an intuitive method for customizing robot navigation.
  • This technique empowers non-experts to tailor robot behavior to specific constraints.
  • The proposed method enhances navigation performance and demonstrator-like trajectory following.