# Learning Whole-body Motor Skills for Humanoids

arXiv: https://arxiv.org/abs/2002.02991 · HTML: https://mlsanity.com/paper/2002.02991/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [HuB: Learning Extreme Humanoid Balance](https://mlsanity.com/paper/2505.07294/) (2025)
- [Learning Bipedal Walking for Humanoids with Current Feedback](https://mlsanity.com/paper/2303.03724/) (2023)
- [Deep Reinforcement Learning for Bipedal Locomotion: A Brief Survey](https://mlsanity.com/paper/2404.17070/) (2024)
- [On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning](https://mlsanity.com/paper/2104.14534/) (2021)
- [Enhancing Model-Based Step Adaptation for Push Recovery through Reinforcement Learning of Step Timing and Region](https://mlsanity.com/paper/2411.01000/) (2024)
- [HuMam: Humanoid Motion Control via End-to-End Deep Reinforcement Learning with Mamba](https://mlsanity.com/paper/2509.18046/) (2025)

## Abstract (arXiv)

This paper presents a hierarchical framework for Deep Reinforcement Learning that acquires motor skills for a variety of push recovery and balancing behaviors, i.e., ankle, hip, foot tilting, and stepping strategies. The policy is trained in a physics simulator with realistic setting of robot model and low-level impedance control that are easy to transfer the learned skills to real robots. The advantage over traditional methods is the integration of high-level planner and feedback control all in one single coherent policy network, which is generic for learning versatile balancing and recovery motions against unknown perturbations at arbitrary locations (e.g., legs, torso). Furthermore, the proposed framework allows the policy to be learned quickly by many state-of-the-art learning algorithms. By comparing our learned results to studies of preprogrammed, special-purpose controllers in the literature, self-learned skills are comparable in terms of disturbance rejection but with additional advantages of producing a wide range of adaptive, versatile and robust behaviors.