# MTAC: Hierarchical Reinforcement Learning-based Multi-gait Terrain-adaptive Quadruped Controller

arXiv: https://arxiv.org/abs/2401.03337 · HTML: https://mlsanity.com/paper/2401.03337/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 1 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [Advanced Skills by Learning Locomotion and Local Navigation End-to-End](https://mlsanity.com/paper/2209.12827/) (2022)
- [Policies Modulating Trajectory Generators](https://mlsanity.com/paper/1910.02812/) (2019)
- [Learning multiple gaits of quadruped robot using hierarchical reinforcement learning](https://mlsanity.com/paper/2112.04741/) (2021)
- [Estimating Achievable Range of Ground Robots Operating on Single Battery Discharge for Operational Efficacy Amelioration](https://mlsanity.com/paper/1811.03159/) (2018)
- [Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors](https://mlsanity.com/paper/2210.04819/) (2022)

## Built on by (most cited first, 1 of 1)

- [MoE-Loco: Mixture of Experts for Multitask Locomotion](https://mlsanity.com/paper/2503.08564/) (2025)

## Abstract (arXiv)

Urban search and rescue missions require rapid first response to minimize loss of life and damage. Often, such efforts are assisted by humanitarian robots which need to handle dynamic operational conditions such as uneven and rough terrains, especially during mass casualty incidents like an earthquake. Quadruped robots, owing to their versatile design, have the potential to assist in such scenarios. However, control of quadruped robots in dynamic and rough terrain environments is a challenging problem due to the many degrees of freedom of these robots. Current locomotion controllers for quadrupeds are limited in their ability to produce multiple adaptive gaits, solve tasks in a time and resource-efficient manner, and require tedious training and manual tuning procedures. To address these challenges, we propose MTAC: a multi-gait terrain-adaptive controller, which utilizes a Hierarchical reinforcement learning (HRL) approach while being time and memory-efficient. We show that our proposed method scales well to a diverse range of environments with similar compute times as state-of-the-art methods. Our method showed greater than 75% on most tasks, outperforming previous work on the majority of test cases.