Emergence of Human-comparable Balancing Behaviors by Deep Reinforcement Learning
This work addresses the problem of improving humanoid robot balancing for robotics applications, though it appears incremental as it builds on existing deep reinforcement learning methods with a new hierarchical and explainable reward design.
The paper tackled humanoid balance control by proposing a hierarchical deep reinforcement learning framework that learns diverse, human-like balancing behaviors, such as active ankle push-off, outperforming conventional zero moment point controllers in under-actuated scenarios.
This paper presents a hierarchical framework based on deep reinforcement learning that learns a diversity of policies for humanoid balance control. Conventional zero moment point based controllers perform limited actions during under-actuation, whereas the proposed framework can perform human-like balancing behaviors such as active push-off of ankles. The learning is done through the design of an explainable reward based on physical constraints. The simulated results are presented and analyzed. The successful emergence of human-like behaviors through deep reinforcement learning proves the feasibility of using an AI-based approach for learning humanoid balancing control in a unified framework.