# Emergence of Human-comparable Balancing Behaviors by Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1809.02074 · HTML: https://mlsanity.com/paper/1809.02074/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning](https://mlsanity.com/paper/2104.14534/) (2021)
- [Characterization of Human Balance through a Reinforcement Learning-based Muscle Controller](https://mlsanity.com/paper/2308.04462/) (2023)
- [The Importance of Models in Data Analysis with Small Human Movement Datasets -- Inspirations from Neurorobotics Applied to Posture Control of Humanoids and Humans](https://mlsanity.com/paper/2102.02543/) (2021)

## Abstract (arXiv)

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.