Self in Space: Benchmarking Self-Awareness and Spatial Cognition in UAV Embodied Intelligence
- Published
- Source
- arXiv
- Paper number
- 624
- Field
- Computer Vision
- arXiv ID
- 2607.12477
Key points
- SIS-Bench evaluates UAV self-spatial understanding by crossing the two axes of spatial cognition and self-awareness with three stages: perception, memory, and reasoning.
- Through expert validation, it created 13 tasks and 4,856 question-answer pairs from 1,646 real UAV videos.
- Current MLLMs were weaker at self-awareness than spatial cognition, and their performance declined progressively from perception to memory and reasoning.
- SIS-Motion, which combines optical flow and visual features, improved perception and memory along both axes, as well as downstream UAV decision-making performance.
- The evaluation mainly remains at the level of video-based question answering and does not cover agents that integrate multiple sensors to carry out actual long-duration autonomous flight.
Paper links
External research summaries. These are not HDATF publications or measured product results.