Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries

Figure 1: Three strategies for instance navigation under an ambiguous user query. (a) Independent Matching (Taioli et al. , 2025 ) scores each candidate independently, causing premature decision to a distractor sharing attributes with the true target. (b) Pooled Independent Matching defers the decision until multiple candidates are collected, but non-discriminative questions still fail to separate similar distractors, while imposing high user burden. (c) Comparative Judgment (Ours) proactively builds a candidate pool and asks binary questions about discriminative attributes derived from candid

Figure 2: Recursive Comparative Judgment. At iteration t , ProCompNav splits the candidate pool U_{t} into a core set G_{c} and a remainder set G_{r} by similarity. It identifies a discriminative attribute a_{t}^{*} , that is common in G_{c} but not in G_{r} . Finally, it asks whether the target has a_{t}^{*} , and prunes the pool to obtain the next candidate pool U_{t+1} based on the user’s response.

Figure 3: Termination-step analysis of AIUTA and ProCompNav. The x-axis shows termination steps in 100-step bins, except the max exploration step; bars (left y-axis) show number of terminated episodes, and lines (right y-axis) show cumulative number of successful episodes.

Figure 4: Qualitative comparison of Independent Matching and Comparative Judgment under a minimally specified query (“Find the dresser”). Independent Matching (left) asks the user to describe an attribute of the intended target without knowing whether that attribute distinguishes it from distractors. This burdens the user with a long response and can lead the robot to mistakenly match a distractor sharing the same attribute. In contrast, Comparative Judgment (right) asks about discriminative attributes in a way that elicits only short user responses, reducing verbal burden while correctly isol

Figure 5: TextNav adaptation of the Recursive Comparison Stage. In TextNav, ProCompNav pre-extracts an attribute set \mathcal{A} from the text goal before the Recursive Comparison Stage and, at each round, selects the discriminative attribute that best supports the current core set G_{c} . Since these attributes are derived from the goal, the active set is always updated to G_{c} . The agent terminates once the target-selection condition is met.

Figure 7: Effect of the candidate pool size threshold N_{\min} that triggers the Recursive Comparison Stage. (Left) SR and SPL under different N_{\min} values. A larger N_{\min} improves comparative judgment and thus increases SR, but leads to lower SPL. (Right) Detailed results for all evaluation metrics, including user burden and navigation cost. Based on this trade-off, we choose N_{\min}=5 as the default setting.