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arxiv: 2503.16548 · v2 · submitted 2025-03-19 · 💻 cs.HC · cs.RO

SemanticScanpath: Combining Gaze and Speech for Situated Human-Robot Interaction Using LLMs

Elisabeth Menendez , Michael Gienger , Santiago Mart\'inez , Carlos Balaguer , Anna Belardinelli This is my paper

Pith reviewed 2026-05-22 23:59 UTC · model grok-4.3

classification 💻 cs.HC cs.RO
keywords gaze scanpathhuman-robot interactionlarge language modelssituated awarenessreferential gazespeech groundingmultimodal input
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The pith

Converting gaze scanpaths to text lets LLMs resolve ambiguous spoken requests for robots in physical scenes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to establish that a semantic text description of a user's eye-movement scanpath, when supplied to a large language model together with spoken words, enables the model to ground underspecified requests in the current physical environment. A sympathetic reader would care because this removes the need for perfectly explicit speech or separate gaze classifiers, letting the same model handle both verbal and nonverbal input. The method shows the model can disregard irrelevant glances and still identify the intended objects across varied tasks. Validation occurs in two scenarios with multiple tasks, where the combined input outperforms control conditions that lack the gaze translation. The approach is demonstrated end-to-end on a physical robot that executes the interpreted request.

Core claim

The paper claims that large language models can reason about referential gaze once the raw scanpath is rendered as text; the resulting combined input allows the model to relate ambiguous speech to the scene and user intent while ignoring spurious glances, yielding higher generality and accuracy than baselines across tasks and scenarios, and permitting direct translation into robot actions.

What carries the argument

The text-based semantic translation of the user's gaze scanpath, which turns eye-movement sequences into a readable description that an LLM processes jointly with speech.

If this is right

  • Robots can interpret requests that mention objects only by gaze rather than by name.
  • The same LLM handles both speech and gaze without separate specialized modules for each modality.
  • Performance holds across different physical layouts and task types without per-task retraining.
  • The system produces executable robot commands directly from the joint interpretation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same translation technique could be tested with other transient signals such as pointing gestures or head orientation.
  • If the translation remains stable under head movement or changing lighting, the method would apply to mobile robots in less controlled spaces.
  • Extending the approach to multi-turn dialogues might allow the model to maintain and update scene references across exchanges.
  • The method suggests that other embodied AI systems could benefit from converting sensor streams into text for unified LLM reasoning.

Load-bearing premise

The semantic translation step converts the raw gaze data into text without distorting or losing the user's actual referential intent.

What would settle it

A controlled trial in which the LLM, given the translated scanpath and speech, repeatedly selects irrelevant objects that appeared only in spurious glances, or shows no accuracy gain over speech-only baselines in new tasks.

Figures

Figures reproduced from arXiv: 2503.16548 by Anna Belardinelli, Carlos Balaguer, Elisabeth Menendez, Michael Gienger, Santiago Mart\'inez.

Figure 1
Figure 1. Figure 1: Top: Example interaction, where the user looks [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Visualization of gaze history and speech input over [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Top row: Accuracy with respect to the ground truth inference in the breakfast (a) and drink (b) scenarios. When the [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Robot demonstration illustrating the LLM’s ability to disambiguate user requests by integrating gaze history and [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
read the original abstract

Large Language Models (LLMs) have substantially improved the conversational capabilities of social robots. Nevertheless, for an intuitive and fluent human-robot interaction, robots should be able to ground the conversation by relating ambiguous or underspecified spoken utterances to the current physical situation and to the intents expressed nonverbally by the user, such as through referential gaze. Here, we propose a representation that integrates speech and gaze to enable LLMs to achieve higher situated awareness and correctly resolve ambiguous requests. Our approach relies on a text-based semantic translation of the scanpath produced by the user, along with the verbal requests. It demonstrates LLMs' capabilities to reason about gaze behavior, robustly ignoring spurious glances or irrelevant objects. We validate the system across multiple tasks and two scenarios, showing its superior generality and accuracy compared to control conditions. We demonstrate an implementation on a robotic platform, closing the loop from request interpretation to execution.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript introduces SemanticScanpath, a system that converts user gaze scanpaths into text-based semantic representations combined with spoken requests, enabling LLMs to resolve ambiguous or underspecified utterances in situated human-robot interaction. It claims this approach demonstrates LLMs' ability to reason about gaze behavior by robustly ignoring spurious glances or irrelevant objects, with validation showing superior generality and accuracy over control conditions across multiple tasks in two scenarios, plus a closed-loop robotic implementation.

Significance. If the quantitative results and implementation details hold, the work could advance situated HRI by providing a general, LLM-based method for multimodal grounding of speech with referential gaze without requiring task-specific models. The engineering integration of gaze-to-text translation is a practical contribution, though the absence of reported metrics, error analysis, or baseline details in the abstract makes the claimed robustness difficult to assess at present.

major comments (2)
  1. [Abstract] Abstract: the central claim of 'superior generality and accuracy' and 'robustly ignoring spurious glances' is asserted without any quantitative metrics, error rates, statistical comparisons, or details on how baselines were implemented or controlled, leaving the evidence for LLM reasoning about gaze unshown and the soundness of the validation unclear.
  2. [Approach description] Approach description (abstract and method): the 'text-based semantic translation of the scanpath' is presented only at a high level with no specification of the procedure, any filtering/summarization rules, heuristics, or potential scenario-specific tuning; without these details it is impossible to determine whether observed performance and robustness should be attributed to LLM reasoning or to the translation pipeline itself.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript to incorporate additional details and metrics as suggested.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim of 'superior generality and accuracy' and 'robustly ignoring spurious glances' is asserted without any quantitative metrics, error rates, statistical comparisons, or details on how baselines were implemented or controlled, leaving the evidence for LLM reasoning about gaze unshown and the soundness of the validation unclear.

    Authors: We agree that the abstract would be strengthened by including key quantitative results. In the revised version, we will update the abstract to summarize specific metrics from the evaluation (such as accuracy and generality comparisons across tasks and scenarios) along with brief notes on baseline controls. The full manuscript already contains these results and statistical details in the validation sections, but adding them to the abstract will make the evidence for the LLM's gaze reasoning more immediately apparent. revision: yes

  2. Referee: [Approach description] Approach description (abstract and method): the 'text-based semantic translation of the scanpath' is presented only at a high level with no specification of the procedure, any filtering/summarization rules, heuristics, or potential scenario-specific tuning; without these details it is impossible to determine whether observed performance and robustness should be attributed to LLM reasoning or to the translation pipeline itself.

    Authors: We acknowledge that the current description of the scanpath-to-text translation is high-level. We will expand the method section in the revision to provide a detailed specification of the translation procedure, including the exact steps for generating the semantic text representation, any filtering or summarization rules applied to the scanpath, and clarification on the absence of scenario-specific tuning. This will help readers distinguish the contributions of the LLM reasoning from the preprocessing steps. revision: yes

Circularity Check

0 steps flagged

No circularity: engineering system description with no derivations, fitted parameters, or self-referential reductions

full rationale

The paper presents an applied integration of gaze scanpath translation into text for LLM-based HRI, with no equations, parameter fitting, uniqueness theorems, or derivation chains. The central claim rests on empirical validation across tasks rather than any mathematical reduction to inputs. No self-citations are invoked as load-bearing premises, and the semantic translation step is described as a representation choice without being redefined in terms of the LLM output it enables. This matches the default expectation of a non-circular engineering paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are stated in the abstract; the central claim rests on the unexamined assumption that LLM reasoning over the generated text will be reliable and that the scanpath-to-text step faithfully encodes intent.

pith-pipeline@v0.9.0 · 5700 in / 1113 out tokens · 16882 ms · 2026-05-22T23:59:33.544798+00:00 · methodology

discussion (0)

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Reference graph

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