# MPC-based Controller with Terrain Insight for Dynamic Legged Locomotion

arXiv: https://arxiv.org/abs/1909.13842 · HTML: https://mlsanity.com/paper/1909.13842/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

## Built on by (most cited first, 8 of 8)

- [PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations](https://mlsanity.com/paper/2207.13224/) (2022)
- [Preference Aligned Diffusion Planner for Quadrupedal Locomotion Control](https://mlsanity.com/paper/2410.13586/) (2024)
- [Learning Semantics-Aware Locomotion Skills from Human Demonstration](https://mlsanity.com/paper/2206.13631/) (2022)
- [Imitation and Adaptation Based on Consistency: A Quadruped Robot Imitates Animals from Videos Using Deep Reinforcement Learning](https://mlsanity.com/paper/2203.05973/) (2022)
- [ZipMPC: Compressed Context-Dependent MPC Cost via Imitation Learning](https://mlsanity.com/paper/2507.13088/) (2025)
- [A Transferable Legged Mobile Manipulation Framework Based on Disturbance Predictive Control](https://mlsanity.com/paper/2203.03391/) (2022)
- [Reinforcement Learning For Quadrupedal Locomotion: Current Advancements And Future Perspectives](https://mlsanity.com/paper/2410.10438/) (2024)
- [Learning Generic and Dynamic Locomotion of Humanoids Across Discrete Terrains](https://mlsanity.com/paper/2405.17227/) (2024)

## Abstract (arXiv)

We present a novel control strategy for dynamic legged locomotion in complex scenarios, that considers information about the morphology of the terrain in contexts when only on-board mapping and computation are available. The strategy is built on top of two main elements: first a contact sequence task that provides safe foothold locations based on a convolutional neural network to perform fast and continuous evaluation of the terrain in search of safe foothold locations; then a model predictive controller that considers the foothold locations given by the contact sequence task to optimize target ground reaction forces. We assess the performance of our strategy through simulations of the hydraulically actuated quadruped robot HyQReal traversing rough terrain under realistic on-board sensing and computing conditions.