Towards Generalizable Visually Grounded Exploration of Household Devices
Recent advancements in Vision-Language Models (VLMs) have demonstrated impressive capabilities in static visual recognition and high-level semantic reasoning. However, current embodied exploration paradigms still heavily rely on imitation learning from human-annotated trajectories, which severely limits agents' generalization ability. The key bottleneck of realizing general autonomous embodied agents lies in Generalizable Visually Grounded Exploration: the ability to operate novel devices without manuals or specific training by actively grounding abstract world knowledge into fine-grained visual affordances. Yet, existing benchmarks fail to evaluate this capability: they generally rely on...
| Authors | Linhao Zheng, Zeming Liu, Wangke Chen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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