mlsanityData to 14 Oct 2026

MuJoCo-Drones-Gym: A GPU-Accelerated Multi-Drone Simulator for Control and Reinforcement Learning

arXiv 2606.08039 ↗First posted 6 Jun 2026cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Robotic simulators are a cornerstone of modern research in aerial robotics, serving both as a vehicle for the development of new control algorithms and as the data source for training reinforcement learning (RL) policies. Yet, existing quadcopter learning environments often face a trade-off between physical fidelity, multi-agent support, and the throughput required by modern deep RL pipelines. In this paper, we present MuJoCo-Drones-Gym, an open-source Gymnasium-compatible multi-drone environment built on top of the MuJoCo physics engine. MuJoCo-Drones-Gym supports an arbitrary number of Bitcraze Crazyflie 2.x nano-quadcopters and exposes a modular API for selecting (i)~the physics model (rigid-body MuJoCo, explicit Python dynamics, or any subset of ground effect, blade drag, and inter-drone downwash), (ii)~the action interface (per-motor RPMs, collective normalized thrust, velocity setpoints, or PID waypoint commands), and (iii)~the observation space (kinematic state vectors, RGB / depth / segmentation cameras, or neighbourhood adjacency information). A PettingZoo ParallelEnv wrapper enables drop-in multi-agent reinforcement learning, while a suite of seven task environments, hover, velocity tracking, multi-drone hover, waypoint navigation, formation flight, gate racing, and a generic multi-agent template, demonstrates the breadth of the interface. We describe the environment design, the underlying physics and quadcopter dynamics, and illustrate its use through control and learning examples that mirror those of the closely related gym-pybullet-drones project, while taking advantage of MuJoCo's improved contact handling, rendering, and parallelizability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 26 references that are in the map. First posted on arXiv 6 Jun 2026.

Builds on

10 of 26 references.

References · 10

  1. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  2. 2019Solving Rubik's Cube with a Robot Handcited by 734
  3. 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 220
  4. 2018Neural Lander: Stable Drone Landing Control using Learned Dynamicscited by 85
  5. 2020Flightmare: A Flexible Quadrotor Simulatorcited by 84
  6. 2021Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Controlcited by 77
  7. 2021safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Roboticscited by 50
  8. 2023OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Controlcited by 33
  9. 2023Control Barrier Functions in Dynamic UAVs for Kinematic Obstacle Avoidance: A Collision Cone Approachcited by 8
  10. 2016Design of a Trajectory Tracking Controller for a Nanoquadcoptercited by 5

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Crazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX
  2. 2025Aerial Gym Simulator: A Framework for Highly Parallelized Simulation of Aerial Robots
  3. 2026Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics
  4. 2025Dreaming Falcon: Physics-Informed Model-Based Reinforcement Learning for Quadcopters
  5. 2025Towards Task-Oriented Flying: Framework, Infrastructure, and Principles
  6. 2025Reactive Aerobatic Flight via Reinforcement Learning
  7. 2025RAPTOR: A Foundation Policy for Quadrotor Control
  8. 2025LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigation