Not What You Asked For: Typographic Attacks in Household Robot Manipulation
Open-vocabulary embodied AI agents increasingly rely on vision-language models such as CLIP for object perception and task grounding. However, the shared embedding space that enables this flexibility introduces a structural vulnerability to typographic attacks, where printed text in a physical scene semantically overrides visual judgment. While prior work has quantified this threat in static 2D benchmarks and 3D navigation tasks, its impact on the full Sense-Plan-Act pipeline of household robot manipulation remains unexplored. This work evaluates typographic attacks in a Habitat-based simulation using the HomeRobot benchmark. We introduce a decoupled perception architecture that exposes a...
| Authors | Ali Iranmanesh, Peng Liu |
|---|---|
| Primary category | cs.CR |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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