Self in Space: Benchmarking Self-Awareness and Spatial Cognition in UAV Embodied Intelligence
Autonomous UAV systems increasingly rely on multimodal large language models (MLLMs) to operate in complex real-world environments. Such embodied scenarios require not only understanding the surrounding space but also maintaining a coherent representation of the agent itself. However, existing UAV-oriented approaches and benchmarks remain largely environment-centric, primarily focusing on spatial understanding tasks, with the agent's self-awareness remaining implicit. To address this gap, we introduce SIS-Bench, a benchmark for evaluating embodied spatial intelligence in UAV scenarios under a unified self-in-space formulation. SIS-Bench organizes evaluation along two complementary dimensi...
| Authors | Zhishan Zou, Guoyan Sun, Zhiwei Wei et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 July 2026 |
| Last updated at source | 15 July 2026 |
Models and datasets referencing this paper
- choucsan/SIS-BenchDataset
- choucsan/OpenUAV-QADataset
- choucsan/SIS-Motion-54KDataset
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