Adaptive Quadruped Balance Control for Dynamic Environments Using Maximum-Entropy Reinforcement Learning.

Area of Science:

• Robotics
• Artificial Intelligence
• Control Systems

Background:

• External disturbances are a major challenge for robot balance in dynamic environments.
• Conventional methods often rely on complex analytical models for robot balancing.
• Adaptability to unpredictable disturbances remains a key research area in legged robotics.

Purpose of the Study:

• To develop a learning-based control architecture for quadrupedal self-balancing.
• To create a system adaptable to unpredictable external disturbances without explicit mathematical modeling.
• To validate the control policy's performance in both simulation and real-world experiments.

Main Methods:

• Utilized reinforcement learning and artificial neural networks to avoid complex analytical modeling.
• Employed a maximum-entropy method (soft actor-critic algorithm) for enhanced exploration and generalization.
• Developed a control policy mapping proprioceptive signals to action commands via a neural network and Tanh Gaussian policy.

Main Results:

• The learning-based policy demonstrated effective self-balancing capabilities in simulation and experiments.
• The control policy showed successful transfer to a real-world quadruped robot with minimal configuration.
• The policy exhibited robustness to vibration conditions not encountered during training.

Conclusions:

• A reinforcement learning-based control architecture offers an effective alternative to analytical models for robot balancing.
• The proposed method enables robust and adaptable quadrupedal self-balancing in dynamic environments.
• The findings highlight the potential for direct real-world application of learned policies in robotics.