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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Quadruped robots need advanced locomotion for complex environments.
  • Current control methods may lack robustness and generalizability.

Purpose of the Study:

  • Implement and evaluate a learning-based controller for quadruped robots.
  • Demonstrate robust and energy-efficient locomotion on the Solo12 platform.

Main Methods:

  • Utilized deep reinforcement learning (DRL) for joint impedance control.
  • Developed an end-to-end learning framework for locomotion control.
  • Focused on policy transfer from simulation to the real robot.

Main Results:

  • Achieved robust velocity tracking and energy-efficient locomotion.
  • Successfully transferred learned policies to the real Solo12 robot.
  • Demonstrated effective performance across diverse indoor and outdoor terrains.

Conclusions:

  • The Solo12 robot is an effective open-source platform for learning-based control research.
  • End-to-end DRL controllers offer a viable path for robust quadrupedal locomotion.
  • The proposed method facilitates easy deployment and transfer of learned locomotion skills.