Benchmarking Interaction-Oriented Aerial Mobility across Heterogeneous Environments

Key points

• 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.1
• Experiments with five representative aerial vision-language-action (VLA) agents and the proposed CrossFly-Agent show that Indoor$\leftrightarrow$Outdoor settings are more challenging than single-environment settings, with the highest success rate reaching 9.7% on unseen scenes.1
• A downstream manipulation case study further validates the relevance of interaction-oriented arrival to subsequent physical interaction.1
• We hope CrossFly helps advance aerial embodied intelligence toward unified mobility and physical interaction in heterogeneous environments.1

Sources (1)

• [1]Benchmarking Interaction-Oriented Aerial Mobility across Heterogeneous Environments
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 01:33 PM

  This work introduces CrossFly, a benchmark for long-horizon interaction-oriented aerial mobility across heterogeneous environments.

  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.

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