[
    {
        "id": "osp-25903",
        "type": "article-journal",
        "title": "Actively Resolving Contextual Uncertainty for Underspecified Tasks in Natural Language",
        "author": [
            {
                "family": "Ravichandran",
                "given": "Zachary"
            },
            {
                "family": "Diller",
                "given": "Jonathan"
            },
            {
                "family": "Cladera",
                "given": "Fernando"
            },
            {
                "family": "Murali",
                "given": "Varun"
            },
            {
                "family": "Pappas",
                "given": "George J."
            },
            {
                "family": "Kumar",
                "given": "Vijay"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/actively-resolving-contextual-uncertainty-for-underspecified-tasks-in-natural-language",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "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 language. CLUE uses an LLM-derived policy to hypothesize task-relevant concepts and potential plans. It then uses a language-embedded map, which is constructed online, to ground these hypotheses into actions. The policy sequentially evaluates hypotheses via closed-loop environment interaction and refines its plans as it gathers new information. We deploy CLUE on a Boston Dynamics Spot across three real indoor and outdoor environments spanning 15 tasks that require object disambiguation, functional inference, and occlusion reasoning. CLUE achieves a success rate within 7 percentage points of an oracle policy and outperforms an LLM-enabled planner without closed-loop feedback by a 4x margin. Supporting experiments demonstrate that simply building and then querying a language-enriched map is insufficient to resolve complex contextual planning tasks; these approaches achieve roughly one third the success rate of CLUE while requiring over 10x more VLM tokens. We provide additional information at https://zacravichandran.github.io/CLUE."
    }
]