Commonsense-Grounded Path Planning from Abstract Instructions
We present \emph{commonsense ranked search} (CoRS), a novel path planner that turns an abstract instruction into a route that follows commonsense. While existing methods respect the considerations written down in advance, a robot working among people must follow those left unstated too, as with a wet floor that a worker avoids without being told. CoRS leverages large language models (LLMs) and vision-language models (VLMs) as commonsense knowledge to reason about these latent considerations in its planning. Given an abstract instruction (\emph{e.g.}, ``move carefully'') and visual observations of each region in the environment, CoRS derives a consideration for each region, as in ``this we...
| Authors | Masafumi Endo, Kohei Honda, Ryo Yonetani |
|---|---|
| Primary category | cs.RO |
| Published | 19 September 2026 |
| Last updated at source | 19 September 2026 |
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