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Related Experiment Video

Updated: Jul 25, 2025

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function
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Navigating to objects in the real world.

Theophile Gervet1, Soumith Chintala2, Dhruv Batra2,3

  • 1Carnegie Mellon University, Pittsburgh, PA, USA.

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Summary
This summary is machine-generated.

Modular learning excels in real-world semantic navigation for mobile robots, achieving a 90% success rate. End-to-end learning struggles due to the sim-to-real gap, highlighting modularity

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Classical spatial navigation pipelines lack semantic understanding for real-world robot deployment.
  • Learning-based approaches, including end-to-end and modular methods, aim to enhance robot navigation.
  • Previous evaluations of visual navigation policies have been limited, primarily in simulation.

Purpose of the Study:

  • To empirically compare semantic visual navigation methods in real-world uncontrolled environments.
  • To evaluate the performance of classical, modular, and end-to-end learning approaches on mobile robots.
  • To identify challenges in current simulation benchmarks for robot navigation.

Main Methods:

  • A large-scale empirical study comparing semantic visual navigation methods.
  • Methods tested include classical, modular learning, and end-to-end learning approaches.
  • Evaluation conducted across six homes without prior mapping or instrumentation.

Main Results:

  • Modular learning achieved a 90% success rate in real-world semantic navigation.
  • End-to-end learning demonstrated a significant performance drop from 77% in simulation to 23% in the real world.
  • The primary cause for end-to-end failure was identified as a large image domain gap between simulation and reality.

Conclusions:

  • Modular learning is a reliable approach for real-world robot navigation to objects, enabling effective sim-to-real transfer.
  • Current simulators are unreliable benchmarks due to significant sim-to-real gaps in images and error modes.
  • Recommendations are provided for improving simulators and advancing research in semantic visual navigation.