# The Cambridge RoboMaster: An Agile Multi-Robot Research Platform

arXiv: https://arxiv.org/abs/2405.02198 · HTML: https://mlsanity.com/paper/2405.02198/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2024.

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

- [Multi-agent Embodied AI: Advances and Future Directions](https://mlsanity.com/paper/2505.05108/) (2025)
- [Language-Conditioned Offline RL for Multi-Robot Navigation](https://mlsanity.com/paper/2407.20164/) (2024)
- [Remotely Detectable Robot Policy Watermarking](https://mlsanity.com/paper/2512.15379/) (2025)
- [JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes](https://mlsanity.com/paper/2505.06771/) (2025)
- [DVM-SLAM: Decentralized Visual Monocular Simultaneous Localization and Mapping for Multi-Agent Systems](https://mlsanity.com/paper/2503.04126/) (2025)
- [Robust and Safe Multi-Agent Reinforcement Learning with Communication for Autonomous Vehicles: From Simulation to Hardware](https://mlsanity.com/paper/2506.00982/) (2025)
- [ReCoDe: Reinforcement Learning-based Dynamic Constraint Design for Multi-Agent Coordination](https://mlsanity.com/paper/2507.19151/) (2025)
- [Prompting Robot Teams with Natural Language](https://mlsanity.com/paper/2509.24575/) (2025)

## Abstract (arXiv)

Compact robotic platforms with powerful compute and actuation capabilities are key enablers for practical, real-world deployments of multi-agent research. This article introduces a tightly integrated hardware, control, and simulation software stack on a fleet of holonomic ground robot platforms designed with this motivation. Our robots, a fleet of customised DJI Robomaster S1 vehicles, offer a balance between small robots that do not possess sufficient compute or actuation capabilities and larger robots that are unsuitable for indoor multi-robot tests. They run a modular ROS2-based optimal estimation and control stack for full onboard autonomy, contain ad-hoc peer-to-peer communication infrastructure, and can zero-shot run multi-agent reinforcement learning (MARL) policies trained in our vectorized multi-agent simulation framework. We present an in-depth review of other platforms currently available, showcase new experimental validation of our system's capabilities, and introduce case studies that highlight the versatility and reliability of our system as a testbed for a wide range of research demonstrations. Our system as well as supplementary material is available online. https://proroklab.github.io/cambridge-robomaster