Actively Resolving Contextual Uncertainty for Underspecified Tasks in Natural Language
Foundation models provide robots with the ability to interpret natural language and reason about environmental context, yet most language-conditioned policies assume that goals are well-specified and that task-relevant information is provided upfront via a prior map. Operating in unfamiliar environments with underspecified tasks entails high contextual uncertainty: the robot must jointly infer what constitutes task success, what constitutes relevant information, and where (or whether) that information exists. We address these limitations via CLUE (Closed-Loop contextual Uncertainty rEsolution), a framework for actively resolving contextual uncertainty given underspecified tasks in natural...
| Authors | Zachary Ravichandran, Jonathan Diller, Fernando Cladera et al. |
|---|---|
| Primary category | cs.RO |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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