# Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Control

arXiv: https://arxiv.org/abs/1909.06586 · HTML: https://mlsanity.com/paper/1909.06586/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2019.

## Built on by (most cited first, 30 of 54)

- [Rapid Locomotion via Reinforcement Learning](https://mlsanity.com/paper/2205.02824/) (2022)
- [Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior](https://mlsanity.com/paper/2212.03238/) (2022)
- [Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion](https://mlsanity.com/paper/2202.05481/) (2022)
- [Fast and Efficient Locomotion via Learned Gait Transitions](https://mlsanity.com/paper/2104.04644/) (2021)
- [Advanced Skills by Learning Locomotion and Local Navigation End-to-End](https://mlsanity.com/paper/2209.12827/) (2022)
- [Learning to Jump from Pixels](https://mlsanity.com/paper/2110.15344/) (2021)
- [Barkour: Benchmarking Animal-level Agility with Quadruped Robots](https://mlsanity.com/paper/2305.14654/) (2023)
- [Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot](https://mlsanity.com/paper/2208.01160/) (2022)
- [CPG-RL: Learning Central Pattern Generators for Quadruped Locomotion](https://mlsanity.com/paper/2211.00458/) (2022)
- [Learning and Adapting Agile Locomotion Skills by Transferring Experience](https://mlsanity.com/paper/2304.09834/) (2023)
- [Learning Free Gait Transition for Quadruped Robots via Phase-Guided Controller](https://mlsanity.com/paper/2201.00206/) (2022)
- [Learning Torque Control for Quadrupedal Locomotion](https://mlsanity.com/paper/2203.05194/) (2022)
- [RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion](https://mlsanity.com/paper/2305.17842/) (2023)
- [Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement Learning](https://mlsanity.com/paper/2210.04435/) (2022)
- [Robust High-speed Running for Quadruped Robots via Deep Reinforcement Learning](https://mlsanity.com/paper/2103.06484/) (2021)
- [Controlling the Solo12 Quadruped Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2309.16683/) (2023)
- [Neural Scene Representation for Locomotion on Structured Terrain](https://mlsanity.com/paper/2206.08077/) (2022)
- [A Learning Framework for Diverse Legged Robot Locomotion Using Barrier-Based Style Rewards](https://mlsanity.com/paper/2409.15780/) (2024)
- [PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations](https://mlsanity.com/paper/2207.13224/) (2022)
- [Cascaded Compositional Residual Learning for Complex Interactive Behaviors](https://mlsanity.com/paper/2212.08954/) (2022)
- [Tuning Legged Locomotion Controllers via Safe Bayesian Optimization](https://mlsanity.com/paper/2306.07092/) (2023)
- [Exploitation-Guided Exploration for Semantic Embodied Navigation](https://mlsanity.com/paper/2311.03357/) (2023)
- [Learning to Walk by Steering: Perceptive Quadrupedal Locomotion in Dynamic Environments](https://mlsanity.com/paper/2209.09233/) (2022)
- [Continuous Versatile Jumping Using Learned Action Residuals](https://mlsanity.com/paper/2304.08663/) (2023)
- [RoboMorph: Evolving Robot Morphology using Large Language Models](https://mlsanity.com/paper/2407.08626/) (2024)
- [Autonomous Navigation for Quadrupedal Robots with Optimized Jumping through Constrained Obstacles](https://mlsanity.com/paper/2107.00773/) (2021)
- [Learning Quadruped Locomotion Policies using Logical Rules](https://mlsanity.com/paper/2107.10969/) (2021)
- [Identifying Important Sensory Feedback for Learning Locomotion Skills](https://mlsanity.com/paper/2306.17101/) (2023)
- [Event Camera-based Visual Odometry for Dynamic Motion Tracking of a Legged Robot Using Adaptive Time Surface](https://mlsanity.com/paper/2305.08962/) (2023)
- [Collaborative Navigation and Manipulation of a Cable-towed Load by Multiple Quadrupedal Robots](https://mlsanity.com/paper/2206.14424/) (2022)

## Abstract (arXiv)

Dynamic legged locomotion is a challenging topic because of the lack of established control schemes which can handle aerial phases, short stance times, and high-speed leg swings. In this paper, we propose a controller combining whole-body control (WBC) and model predictive control (MPC). In our framework, MPC finds an optimal reaction force profile over a longer time horizon with a simple model, and WBC computes joint torque, position, and velocity commands based on the reaction forces computed from MPC. Unlike existing WBCs, which attempt to track commanded body trajectories, our controller is focused more on the reaction force command, which allows it to accomplish high speed dynamic locomotion with aerial phases. The newly devised WBC is integrated with MPC and tested on the Mini-Cheetah quadruped robot. To demonstrate the robustness and versatility, the controller is tested on six different gaits in a number of different environments, including outdoors and on a treadmill, reaching a top speed of 3.7 m/s.