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Adaptive Question Generation for Ambiguous Robot Instructions Based on Information Gain and Human Cognitive Characteristics

Adaptive Question Generation for Ambiguous Robot Instructions Based on Information Gain and Human Cognitive Characteristics

Ryo Terashima, Yuga Yano, Hakaru Tamukoh · N/A · 2026

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Summary

In this study, we propose an adaptive question generation mechanism that addresses uncertainty arising from ambiguous instructions. Natural-language instructions for service robots often contain ambiguities.

Abstract Summary

In this study, we propose an adaptive question generation mechanism that addresses uncertainty arising from ambiguous instructions. Natural-language instructions for service robots often contain ambiguities. In scenes with multiple similar objects, the robot must identify the target object by asking questions. However, existing question generation methods focus on probabilistic discrimination efficiency and do not sufficiently account for user ease of response. Asking questions without considering the user's ease of response may cause incorrect or uncertain answers. Such incorrect or uncertain answers lead to additional clarification turns and degraded dialogue efficiency. The proposed mechanism integrates probabilistic discrimination efficiency and human cognitive characteristics to generate questions that users can intuitively and easily answer. First, the proposed mechanism extracts visual attributes of candidate objects using a vision-language model (VLM). Next, the proposed mechanism computes the information gain (IG) of each attribute and selects the visual attribute (e.g., color, shape) that most reduces uncertainty in the candidate set. When multiple attributes have the same IG, the proposed mechanism applies a visual attribute filter based on cognitive characteristics to select the visual attribute that users can most intuitively perceive. The proposed mechanism then prompts the VLM with the selected attribute and the corresponding attribute values of each candidate object to generate a concise natural-language question. This process is repeated until the target object is uniquely identified. The proposed mechanism achieved a target identification success rate of 88.5% in the evaluation using a dataset of 120 scenes. Furthermore, the proposed mechanism received high ratings in subjective evaluations of response ease.

Key Points

  • In this study, we propose an adaptive question generation mechanism that addresses uncertainty arising from ambiguous instructions.
  • Natural-language instructions for service robots often contain ambiguities
  • In scenes with multiple similar objects, the robot must identify the target object by asking questions.
  • However, existing question generation methods focus on probabilistic discrimination efficiency and do not sufficiently account for user ease of response.
  • Asking questions without considering the user's ease of response may cause incorrect or uncertain answers.

PROPOSAL

The proposed mechanism aims to uniquely identify the target object through conversation in response to ambiguous object search instructions, given only the target category. In this section, we define the visual attributes used in the proposed mechanism, then describe the details of each component. Visual attributes are features extracted from the appearance of objects that distinguish among candidate objects.

PROPOSAL — Adaptive Question Generation for Ambiguous Robot Instructions Based on Information Gain and Human Cognitive Characteristics
Fig. 1: A scene in which a robot receives an ambiguous instruction and attempts to identify the target object from multiple candidate objects. Left (red region, Without Proposed): The case without the proposed mechanism, where the user hesitates to answer and the target object cannot be identified. Right (green region, With Proposed): The case with the proposed mechanism, where a concise question enables efficient ambiguity resolution.

EXPERIMENTAL SETTINGS

This section describes the experimental setup for evaluating the effectiveness of the proposed adaptive question generation mechanism. We demonstrate the effectiveness of the proposed mechanism from two perspectives, assuming ambiguous natural language instructions: (1) the efficiency of ambiguity resolution through dialogue, and (2) the ease of response to questions for users.

We conducted large-scale user experiments targeting a broad range of general users through crowd-sourcing.

EXPERIMENTAL SETTINGS — Adaptive Question Generation for Ambiguous Robot Instructions Based on Information Gain and Human Cognitive Characteristics
Fig. 2: Overview of the adaptive question generation mechanism based on information gain and human cognitive characteristics.

EXPERIMENTAL RESULTS

Attributes that users can intuitively recall are considered to enable prompt responses when asked about them. Therefore, we use the questionnaire’s ranking as an indicator of ease of response. Table II shows the mean rank of each visual attribute computed from the questionnaire responses. Overall, Text and Color ranked highly across many categories. This ordering is consistent with prior accounts of saliency.

Perceptually salient features such as color and text are recalled preferentially [ 16 , 19 ] .

EXPERIMENTAL RESULTS — Adaptive Question Generation for Ambiguous Robot Instructions Based on Information Gain and Human Cognitive Characteristics
Fig. 3: Overview of target candidate and visual attribute extraction.

Sources and Demonstrations

Figures are reproduced from Terashima et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.

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