SkyAgent: Revisable Target Memory for Continuous Aerial Search
Continuous aerial search builds target hypotheses from sparse, viewpoint-dependent observations.
ProofPaper ↗
Key points
- When later evidence contradicts a stored hypothesis, however, the candidate can remain available to the planner and continue triggering approach or verification actions.
- We call this trajectory-level failure false candidate persistence (FCP).
- We equip SkyAgent with Revisable Target Memory (RTM), which associates evidence with persistent candidates, discounts correlated views, and revises each candidate through Tentative, Confirmed, and Rejected states.
- Across 35 matched real-world DJI scenarios per method, RTM consistently improves task success and behavioral reliability, further validating its state-to-action correction under physical deployment conditions.
Sources (1)
- [1]SkyAgent: Revisable Target Memory for Continuous Aerial SearcharXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 11:00 AM
Continuous aerial search builds target hypotheses from sparse, viewpoint-dependent observations.
When later evidence contradicts a stored hypothesis, however, the candidate can remain available to the planner and continue triggering approach or verification actions.
Extractive summary: sentences quoted from the sources.
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