ECHO: Embodied Camera Observations of Human Object Carrying
Embodied and assistive agents must do more than recognize objects: they must reason about where an object belongs given the layout of an environment and the habits of the people who live in it. Progress on this problem has been limited, in part because no dedicated benchmark or dataset exists to define and evaluate it. Existing RGB-D scan datasets reconstruct static rooms without human activity, while human-object-interaction datasets capture motion without a navigable, fully reconstructed scene or a ground-truth notion of an object's natural destination. We introduce contextual object placement as a benchmark task: predicting an object's destination during an observed object-carrying epi...
| Authors | Xuefei Sun, Lorin Achey, Kali Hamilton et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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