CARLA-Air: Fly Drones Inside a CARLA World -- A Unified Infrastructure for Air-Ground Embodied Intelligence

• URL: http://arxiv.org/abs/2603.28032v1
• Date: Mon, 30 Mar 2026 04:49:29 GMT
• Title: CARLA-Air: Fly Drones Inside a CARLA World -- A Unified Infrastructure for Air-Ground Embodied Intelligence
• Abstract summary: We present CARLA-Air, an open-source infrastructure that unifies high-fidelity urban driving and physics-accurate multirotor flight within a single Unreal Engine process.<n>Within a shared physics tick and rendering pipeline, CARLA-Air delivers environments with rule-compliant traffic, socially-aware pedestrians, and aerodynamically consistent UAV dynamics.
• Score: 2.061094726550731
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The convergence of low-altitude economies, embodied intelligence, and air-ground cooperative systems creates growing demand for simulation infrastructure capable of jointly modeling aerial and ground agents within a single physically coherent environment. Existing open-source platforms remain domain-segregated: driving simulators lack aerial dynamics, while multirotor simulators lack realistic ground scenes. Bridge-based co-simulation introduces synchronization overhead and cannot guarantee strict spatial-temporal consistency. We present CARLA-Air, an open-source infrastructure that unifies high-fidelity urban driving and physics-accurate multirotor flight within a single Unreal Engine process. The platform preserves both CARLA and AirSim native Python APIs and ROS 2 interfaces, enabling zero-modification code reuse. Within a shared physics tick and rendering pipeline, CARLA-Air delivers photorealistic environments with rule-compliant traffic, socially-aware pedestrians, and aerodynamically consistent UAV dynamics, synchronously capturing up to 18 sensor modalities across all platforms at each tick. The platform supports representative air-ground embodied intelligence workloads spanning cooperation, embodied navigation and vision-language action, multi-modal perception and dataset construction, and reinforcement-learning-based policy training. An extensible asset pipeline allows integration of custom robot platforms into the shared world. By inheriting AirSim's aerial capabilities -- whose upstream development has been archived -- CARLA-Air ensures this widely adopted flight stack continues to evolve within a modern infrastructure. Released with prebuilt binaries and full source: https://github.com/louiszengCN/CarlaAir

Related papers

• HERCULES: An Open-Source Simulation Framework for Heterogeneous Multi-Robot SLAM, Collaborative Perception, and Exploration [3.15872644487767]
  HERCULES is an open-source simulator and data-collection pipeline for heterogeneous multi-robot autonomy.<n>Built upon the Unreal Engine 5 (UE5)-based simulators AirSim and Cosys-AirSim, HERCULES resolves key architectural limitations of prior frameworks.<n>Our experiments in heterogeneous multi-robot SLAM, collaborative perception, and exploration, using both HERCULES-generated data and active closed-loop execution, demonstrate its utility for advancing heterogeneous multi-robot autonomy.
  arXiv  Detail & Related papers  (2026-06-22T01:51:40Z)
• Kairos: A Native World Model Stack for Physical AI [66.80630645291795]
  We introduce Kairos, a native world model stack designed around these requirements.<n>Kairos learns the world by pioneering a Native Pre-training governed by a Cross-Embodiment Paradigm Data Curriculum.<n>Kairos maintains the world by unified world understanding, generation, and prediction within a Native Unified Architecture equipped with Hybrid Linear Temporal Attention.
  arXiv  Detail & Related papers  (2026-06-15T10:37:42Z)
• Can Aerial VLA Models Cooperate? Evaluating Closed-Loop Air-Ground Coordination with CARLA-Air [2.061094726550731]
  We study whether single-UAV capabilities can transfer to air-ground cooperation.<n>We use CARLA-Air, a single-process evaluation environment that unifies CARLA and AirSim inside one Unreal Engine runtime.<n>Results show that current aerial VLA models can often track or follow a ground partner, but struggle to convert this single-agent competence into stable cooperative behavior.
  arXiv  Detail & Related papers  (2026-05-29T09:36:08Z)
• MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving [62.43744546817599]
  Vision-language--action (VLA) models are effective as end-to-end motion planners, but can be brittle when evaluated in closed-loop settings.<n>We propose MAPLE, a novel framework for reactive, multi-agent rollout of a dynamic driving scenario in the latent space of the VLA model.<n> MAPLE achieves state-of-the-art driving performance on Bench2Drive and demonstrates scalable, closed-loop multi-agent play for robust E2E autonomous driving systems.
  arXiv  Detail & Related papers  (2026-05-13T23:35:14Z)
• AirSimAG: A High-Fidelity Simulation Platform for Air-Ground Collaborative Robotics [9.533422686927908]
  We present AirsimAG, a high-fidelity air-ground collaborative simulation platform built upon an extensively customized AirSim framework.<n>The platform enables synchronized multi-agent simulation and supports heterogeneous sensing and control interfaces for UAV-UGV systems.
  arXiv  Detail & Related papers  (2026-03-24T11:17:53Z)
• aerial-autonomy-stack -- a Faster-than-real-time, Autopilot-agnostic, ROS2 Framework to Simulate and Deploy Perception-based Drones [0.3262230127283452]
  We introduce aerial-autonomy-stack, an open-source, end-to-end framework designed to streamline the pipeline from ( GPU-accelerated) perception to (flight controller-based) action.<n>We show that it supports over 20x faster-than-real-time, end-to-end simulation of a complete development and deployment stack.
  arXiv  Detail & Related papers  (2026-02-06T23:29:33Z)
• Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation [69.30586607892842]
  We introduce Genie Envisioner (GE), a unified world foundation platform for robotic manipulation.<n>GE integrates policy learning, evaluation, and simulation within a single video-generative framework.
  arXiv  Detail & Related papers  (2025-08-07T17:59:44Z)
• Sky-Drive: A Distributed Multi-Agent Simulation Platform for Human-AI Collaborative and Socially-Aware Future Transportation [8.821410584051648]
  This paper introduces Sky-Drive, a novel distributed multi-agent simulation platform.<n>Sky-Drive supports diverse applications such as autonomous vehicle-human road users interaction modeling, human-in-the-loop training, socially-aware reinforcement learning, personalized driving development, and customized scenario generation.
  arXiv  Detail & Related papers  (2025-04-25T01:33:26Z)
• A General Infrastructure and Workflow for Quadrotor Deep Reinforcement Learning and Reality Deployment [48.90852123901697]
  We propose a platform that enables seamless transfer of end-to-end deep reinforcement learning (DRL) policies to quadrotors.<n>Our platform provides rich types of environments including hovering, dynamic obstacle avoidance, trajectory tracking, balloon hitting, and planning in unknown environments.
  arXiv  Detail & Related papers  (2025-04-21T14:25:23Z)
• AirFogSim: A Light-Weight and Modular Simulator for UAV-Integrated Vehicular Fog Computing [18.03227366452815]
  We present the design and implementation of AirFogSim, a lightweight and modular aerial-ground collaborative simulation platform. A multifaceted use case is carried out to validate AirFogSim's effectiveness, including UAV trajectory, task offloading, resource allocation, and blockchain. AirFogSim is envisioned to set a new precedent in the UAV-integrated VFC simulation, bridge the gap between theoretical design and practical validation, and pave the way for future intelligent transportation domains.
  arXiv  Detail & Related papers  (2024-09-04T08:26:14Z)
• Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks [93.38375271826202]
  We present a method to improve generalization and robustness to distribution shifts in sim-to-real visual quadrotor navigation tasks. We first build a simulator by integrating Gaussian splatting with quadrotor flight dynamics, and then, train robust navigation policies using Liquid neural networks. In this way, we obtain a full-stack imitation learning protocol that combines advances in 3D Gaussian splatting radiance field rendering, programming of expert demonstration training data, and the task understanding capabilities of Liquid networks.
  arXiv  Detail & Related papers  (2024-06-21T13:48:37Z)
• VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles [131.2240621036954]
  We present VISTA, an open source, data-driven simulator that integrates multiple types of sensors for autonomous vehicles. Using high fidelity, real-world datasets, VISTA represents and simulates RGB cameras, 3D LiDAR, and event-based cameras. We demonstrate the ability to train and test perception-to-control policies across each of the sensor types and showcase the power of this approach via deployment on a full scale autonomous vehicle.
  arXiv  Detail & Related papers  (2021-11-23T18:58:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.