# Learning Bipedal Robot Locomotion from Human Movement

arXiv: https://arxiv.org/abs/2105.12277 · HTML: https://mlsanity.com/paper/2105.12277/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Learning Agile Robotic Locomotion Skills by Imitating Animals](https://mlsanity.com/paper/2004.00784/) (2020)
- [Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience](https://mlsanity.com/paper/1810.05687/) (2018)
- [Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?](https://mlsanity.com/paper/1611.01055/) (2016)
- [Feedback Control For Cassie With Deep Reinforcement Learning](https://mlsanity.com/paper/1803.05580/) (2018)
- [Iterative Reinforcement Learning Based Design of Dynamic Locomotion Skills for Cassie](https://mlsanity.com/paper/1903.09537/) (2019)

## Built on by (most cited first, 7 of 7)

- [Physics-Based Motion Imitation with Adversarial Differential Discriminators](https://mlsanity.com/paper/2505.04961/) (2025)
- [Sim-to-Real Learning for Bipedal Locomotion Under Unsensed Dynamic Loads](https://mlsanity.com/paper/2204.04340/) (2022)
- [Deep Reinforcement Learning for Bipedal Locomotion: A Brief Survey](https://mlsanity.com/paper/2404.17070/) (2024)
- [Achieving Stable High-Speed Locomotion for Humanoid Robots with Deep Reinforcement Learning](https://mlsanity.com/paper/2409.16611/) (2024)
- [Optimizing Bipedal Locomotion for The 100m Dash With Comparison to Human Running](https://mlsanity.com/paper/2508.03070/) (2025)
- [Learning Bipedal Locomotion on Gear-Driven Humanoid Robot Using Foot-Mounted IMUs](https://mlsanity.com/paper/2504.00614/) (2025)
- [Improving Wind Resistance Performance of Cascaded PID Controlled Quadcopters using Residual Reinforcement Learning](https://mlsanity.com/paper/2308.01648/) (2023)

## Abstract (arXiv)

Teaching an anthropomorphic robot from human example offers the opportunity to impart humanlike qualities on its movement. In this work we present a reinforcement learning based method for teaching a real world bipedal robot to perform movements directly from human motion capture data. Our method seamlessly transitions from training in a simulation environment to executing on a physical robot without requiring any real world training iterations or offline steps. To overcome the disparity in joint configurations between the robot and the motion capture actor, our method incorporates motion re-targeting into the training process. Domain randomization techniques are used to compensate for the differences between the simulated and physical systems. We demonstrate our method on an internally developed humanoid robot with movements ranging from a dynamic walk cycle to complex balancing and waving. Our controller preserves the style imparted by the motion capture data and exhibits graceful failure modes resulting in safe operation for the robot. This work was performed for research purposes only.