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Why a robot might need to ask a question
A request such as “pick up the mug” can be clear when there is one mug in view and ambiguous when there are several. A language-model planner may still produce a polished, decisive instruction, even though the wording does not identify which object the person means. Acting on the wrong interpretation could produce an unwanted result.
That gap between fluent output and dependable understanding motivates KnowNo. Princeton Engineering describes the approach as a way to make a robot recognize when it does not know enough to proceed. The goal is not human-like self-awareness; it is a practical decision about whether a proposed plan is sufficiently certain or whether the robot should ask for clarification. Princeton Engineering’s explanation gives the everyday example of instructions that do not distinguish among several objects.
How KnowNo estimates uncertainty
KnowNo applies conformal prediction to a large language model (LLM) planner. In broad terms, conformal prediction is used to form a set of plausible predictions rather than relying on one answer alone. The method uses that uncertainty information to help determine whether the robot should act or request human input.
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This matters because a planner can sound confident while making a mistaken prediction. KnowNo is designed to connect uncertainty to a decision: continue when the available information is adequate, or ask for help when uncertainty is high enough to make proceeding undesirable. The paper frames this as balancing task completion against the amount of assistance requested, not as eliminating all mistakes.
What the experiments evaluated
The KnowNo paper reports experiments in simulated and real-robot setups. It considers several kinds of ambiguity, including spatial and numeric uncertainty, human preferences, and Winograd schemas—language problems where resolving a reference depends on context. Princeton’s account says the work was tested with a simulated robotic arm and two types of robot hardware.
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The authors describe statistical guarantees on task completion under the method and its assumptions. Those guarantees should be read in that setting: they do not establish a universal safety guarantee for arbitrary robots, instructions, environments, or deployments. The reported experiments show how the approach performed in the evaluated research setups; they do not prove broad readiness for unsupervised use.
What KnowNo does—and does not—establish
- It targets a specific problem: deciding whether an LLM-generated robot plan is uncertain enough to warrant clarification.
- It treats asking as a trade-off: enough human help to achieve the desired task success, while avoiding unnecessary requests.
- It was evaluated across multiple ambiguity types: the paper reports both simulated and physical robot experiments.
- It is not proof of general robot safety: statistical assurances are conditional on the paper’s method and assumptions, and the experiments do not cover every real-world condition.
- It does not imply that every consumer robot uses KnowNo: the project presents a research framework, not a general feature claim about current robots.
Where to read more
The full paper, “Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners,” appeared in the Proceedings of the 7th Conference on Robot Learning (CoRL 2023), in Proceedings of Machine Learning Research, volume 229, pages 661–682. The paper in Proceedings of Machine Learning Research contains the technical account. The KnowNo project page identifies it as a CoRL 2023 Best Student Paper and provides links to the paper, video, code, and demo.
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