Dynamic Fall Recovery Control for Legged Robots via Reinforcement Learning.

Area of Science:

• Robotics
• Artificial Intelligence
• Control Systems

Background:

• Legged robots face inevitable falls in unstructured environments.
• Existing methods struggle with complex, unspecified contacts during fall recovery.
• Dynamic recovery is crucial for uninterrupted robot locomotion.

Purpose of the Study:

• To develop a novel framework for dynamic fall recovery in legged robots.
• To address challenges posed by external disturbances and unspecified contacts.
• To enable robust locomotion despite falling events.

Main Methods:

• Introduced a deep reinforcement learning framework.
• Trained a learning-based state estimator.
• Developed a proprioceptive history policy for dynamic fall recovery.
• Applied the framework to various indoor and outdoor fall scenarios.

Main Results:

• The proposed framework effectively trains robots for dynamic fall recovery.
• Learned policies demonstrated hardware feasibility and real-world implementation.
• Extensive trials with a quadruped robot confirmed high effectiveness on flat surfaces and grassland.

Conclusions:

• The novel deep reinforcement learning framework significantly improves dynamic fall recovery capabilities in legged robots.
• The approach offers a robust solution for real-world robotic applications.
• This research paves the way for more resilient and autonomous legged robots.