Benchmarking Interaction-Oriented Aerial Mobility across Heterogeneous Environments
cs.RO
Submitted: 2026-10-13
Updated: 2026-10-13
Code: https://github.com/huggingface/lerobot
Project page: https://bozhistudying233.github.io/CrossFly
License: http://creativecommons.org/licenses/by/4.0/
The gist: Aerial mobile manipulation requires unmanned aerial vehicles (UAVs) to traverse heterogeneous environments such as indoor and outdoor domains, and connect navigation with subsequent physical
Terminology
Abstract
Aerial mobile manipulation requires unmanned aerial vehicles (UAVs) to traverse heterogeneous environments such as indoor and outdoor domains, and connect navigation with subsequent physical interaction within the same trajectories. Existing aerial benchmarks are typically confined to single-environment settings, limiting evaluation of agents across continuous environmental transitions. When interaction is evaluated, agents are often initialized near the target, leaving the transition from navigation to interaction largely unassessed. This work introduces CrossFly, a benchmark for long-horizon interaction-oriented aerial mobility across heterogeneous environments. CrossFly contains 15K trajectories across more than 20 reconstructed scenes under a unified simulation and evaluation framework, covering Indoor, Outdoor, and Indoor Outdoor trajectories. It further introduces Interaction Readiness Rate (IRR) to assess whether terminal navigation states provide suitable conditions for downstream interaction without assuming a specific manipulation policy. Experiments with five representative aerial vision-language-action (VLA) agents and the proposed CrossFly-Agent show that Indoor Outdoor settings are more challenging than single-environment settings, with the highest success rate reaching 9.7% on unseen scenes. A downstream manipulation case study further validates the relevance of interaction-oriented arrival to subsequent physical interaction. We hope CrossFly helps advance aerial embodied intelligence toward unified mobility and physical interaction in heterogeneous environments.
Sources
- AIR-VLA: Vision-Language-Action Systems for Aerial Manipulation
- AIR-VLA+: Decoupling Movement and Manipulation via Cascaded Dual-Action Decoders with Asymmetric MoE for Aerial Robots
- NavVerse: Benchmarking Indoor-to-Outdoor Embodied Navigation in Continuous Robot Simulation
- HUGE-Bench: A Benchmark for High-Level UAV Vision-Language-Action Tasks
- LongFly: Long-Horizon UAV Vision-and-Language Navigation with Spatiotemporal Context Integration
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- FAST: Efficient Action Tokenization for Vision-Language-Action Models
- Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing
- Qwen3 Technical Report
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies