# Human Motion Control of Quadrupedal Robots using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2204.13336 · HTML: https://mlsanity.com/paper/2204.13336/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2022.

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

- [Visual Whole-Body Control for Legged Loco-Manipulation](https://mlsanity.com/paper/2403.16967/) (2024)
- [Cascaded Compositional Residual Learning for Complex Interactive Behaviors](https://mlsanity.com/paper/2212.08954/) (2022)
- [Pose-aware Attention Network for Flexible Motion Retargeting by Body Part](https://mlsanity.com/paper/2306.08006/) (2023)
- [Physics-based Motion Retargeting from Sparse Inputs](https://mlsanity.com/paper/2307.01938/) (2023)
- [Mollification Effects of Policy Gradient Methods](https://mlsanity.com/paper/2405.17832/) (2024)
- [MoReFlow: Motion Retargeting Learning through Unsupervised Flow Matching](https://mlsanity.com/paper/2509.25600/) (2025)

## Abstract (arXiv)

A motion-based control interface promises flexible robot operations in dangerous environments by combining user intuitions with the robot's motor capabilities. However, designing a motion interface for non-humanoid robots, such as quadrupeds or hexapods, is not straightforward because different dynamics and control strategies govern their movements. We propose a novel motion control system that allows a human user to operate various motor tasks seamlessly on a quadrupedal robot. We first retarget the captured human motion into the corresponding robot motion with proper semantics using supervised learning and post-processing techniques. Then we apply the motion imitation learning with curriculum learning to develop a control policy that can track the given retargeted reference. We further improve the performance of both motion retargeting and motion imitation by training a set of experts. As we demonstrate, a user can execute various motor tasks using our system, including standing, sitting, tilting, manipulating, walking, and turning, on simulated and real quadrupeds. We also conduct a set of studies to analyze the performance gain induced by each component.