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An End-to-End Deep Reinforcement Learning-Based Intelligent Agent Capable of Autonomous Exploration in Unknown

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This study introduces an autonomous exploration algorithm for social robots, enabling them to adapt to new environments. The algorithm was successfully tested in both simulation and real-world robots, demonstrating robust performance.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Social robots are increasingly integrated into daily life for companionship and assistance.
  • Unlike industrial robots, social robots require autonomy and adaptability to dynamic environments.
  • Advancements in artificial intelligence and machine learning are driving the development of more capable robots.

Purpose of the Study:

  • To develop an algorithm for autonomous exploration and adaptation in unknown environments for social robots.
  • To enhance robot autonomy, a critical feature for human-interactive robots.
  • To bridge the gap between simulated robot capabilities and real-world performance.

Main Methods:

  • Developed a novel algorithm for autonomous environmental exploration and adaptation.
  • Implemented and tested the algorithm in a simulated robot environment.
  • Validated the algorithm's performance on a physical robot, incorporating sensor fusion techniques.

Main Results:

  • The algorithm demonstrated successful autonomous exploration and adaptation in simulated unknown environments.
  • Sensor fusion techniques effectively mitigated real-world sensor noise during exploration.
  • The implemented algorithm achieved robust exploration performance on a physical robot.

Conclusions:

  • The developed algorithm significantly contributes to creating highly autonomous social robots.
  • The approach enables robots to learn and adapt effectively in unpredictable, real-world settings.
  • This research paves the way for more sophisticated and reliable human-robot interaction.