# Toward Scalable Multirobot Control: Fast Policy Learning in Distributed MPC

arXiv: https://arxiv.org/abs/2412.19669 · HTML: https://mlsanity.com/paper/2412.19669/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 65 references that are in the map.
- First posted on arXiv 27 Dec 2024.

## Builds on (most cited first, 24 of 24)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks](https://mlsanity.com/paper/1903.08792/) (2019)
- [Graph Neural Networks for Decentralized Multi-Robot Path Planning](https://mlsanity.com/paper/1912.06095/) (2019)
- [Safe Policies for Reinforcement Learning via Primal-Dual Methods](https://mlsanity.com/paper/1911.09101/) (2019)
- [Safe Reinforcement Learning Using Robust Control Barrier Functions](https://mlsanity.com/paper/2110.05415/) (2021)
- [Learning to Play Table Tennis From Scratch using Muscular Robots](https://mlsanity.com/paper/2006.05935/) (2020)
- [Policy Search for Model Predictive Control with Application to Agile Drone Flight](https://mlsanity.com/paper/2112.03850/) (2021)
- [Barrier-Certified Adaptive Reinforcement Learning with Applications to Brushbot Navigation](https://mlsanity.com/paper/1801.09627/) (2018)
- [Reinforcement Learned Distributed Multi-Robot Navigation with Reciprocal Velocity Obstacle Shaped Rewards](https://mlsanity.com/paper/2203.10229/) (2022)
- [Robust Tube-based Model Predictive Control with Koopman Operators--Extended Version](https://mlsanity.com/paper/2108.13011/) (2021)
- [Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning](https://mlsanity.com/paper/2203.04700/) (2022)
- [Distributed Model Predictive Control for Heterogeneous Vehicle Platoons under Unidirectional Topologies](https://mlsanity.com/paper/1603.03225/) (2016)
- [Distributed Learning of Decentralized Control Policies for Articulated Mobile Robots](https://mlsanity.com/paper/1901.08537/) (2019)
- [Learning-Based Predictive Control via Real-Time Aggregate Flexibility](https://mlsanity.com/paper/2012.11261/) (2020)
- [Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehicles](https://mlsanity.com/paper/2112.11217/) (2021)
- [Robust Learning-based Predictive Control for Discrete-time Nonlinear Systems with Unknown Dynamics and State Constraints](https://mlsanity.com/paper/1911.09827/) (2019)
- [Consensus-based Distributed Optimization Enhanced by Integral Feedback](https://mlsanity.com/paper/2111.09346/) (2021)
- [Distributed Nonlinear MPC of Multi-Agent Systems with Data Compression and Random Delays - Extended Version](https://mlsanity.com/paper/1504.03494/) (2015)
- [A hierarchical multirate MPC scheme for interconnected systems - textitextended version](https://mlsanity.com/paper/1705.08818/) (2017)
- [Fully distributed cooperation for networked uncertain mobile manipulators](https://mlsanity.com/paper/1809.07642/) (2018)
- [Implementation of UAV Coordination Based on a Hierarchical Multi-UAV Simulation Platform](https://mlsanity.com/paper/2005.01125/) (2020)
- [Explicit Distributed and Localized Model Predictive Control via System Level Synthesis](https://mlsanity.com/paper/2005.13807/) (2020)
- [ALADIN-α -- An open-source MATLAB toolbox for distributed non-convex optimization](https://mlsanity.com/paper/2006.01866/) (2020)

## Built on by (most cited first, 2 of 2)

- [RAY-TOLD: Ray-Based Latent Dynamics for Dense Dynamic Obstacle Avoidance with TDMPC](https://mlsanity.com/paper/2604.27450/) (2026)
- [Distributed Secure Learning Control for Large-scale Multirobots under Stealthy Actuator Attacks](https://mlsanity.com/paper/2609.06896/) (2026)

## Abstract (arXiv)

Distributed model predictive control (DMPC) is promising in achieving optimal cooperative control in multirobot systems (MRS). However, real-time DMPC implementation relies on numerical optimization tools to periodically calculate local control sequences online. This process is computationally demanding and lacks scalability for large-scale, nonlinear MRS. This article proposes a novel distributed learning-based predictive control (DLPC) framework for scalable multirobot control. Unlike conventional DMPC methods that calculate open-loop control sequences, our approach centers around a computationally fast and efficient distributed policy learning algorithm that generates explicit closed-loop DMPC policies for MRS without using numerical solvers. The policy learning is executed incrementally and forward in time in each prediction interval through an online distributed actor-critic implementation. The control policies are successively updated in a receding-horizon manner, enabling fast and efficient policy learning with the closed-loop stability guarantee. The learned control policies could be deployed online to MRS with varying robot scales, enhancing scalability and transferability for large-scale MRS. Furthermore, we extend our methodology to address the multirobot safe learning challenge through a force field-inspired policy learning approach. We validate our approach's effectiveness, scalability, and efficiency through extensive experiments on cooperative tasks of large-scale wheeled robots and multirotor drones. Our results demonstrate the rapid learning and deployment of DMPC policies for MRS with scales up to 10,000 units.