Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning

Understand

The ability to recover from a fall is an essential feature for a legged robot to navigate in challenging environments robustly.

• Until today, there has been very little progress on this topic.
• Current solutions mostly build upon (heuristically) predefined trajectories, resulting in unnatural behaviors and requiring considerable effort in engineering system-specific components.
• In this paper, we present an approach based on model-free Deep Reinforcement Learning (RL) to control recovery maneuvers of quadrupedal robots using a hierarchical behavior-based controller.