Emergence of Human-comparable Balancing Behaviors by Deep Reinforcement Learning

Abstract

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.

References in corpus (1)

• Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?

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