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REVIEW 3 major objections 5 minor 45 references

AI or Human? Understanding Perceptions of Embodied Robots with LLMs

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that people cannot reliably tell whether an embodied robot is steered by a large language model or by a human teleoperator, and that they systematically over-attribute human behavior to AI.

desk verdict First embodied verbal Turing Test with an LLM-driven robot against a teleoperated human; the null result and misidentification asymmetry are worth attention, but the two-person baseline and clustered statistics keep it from being definitive. read the letter →

arxiv 2507.16398 v1 pith:B65OM2D5 submitted 2025-07-22 cs.RO cs.HC

Lavinia Hriscu , Alberto Sanfeliu , Anais Garrell This is my paper
classification cs.ROcs.HC
keywords embodiedTuringtesthuman-robotinteractionlargelanguagemodelsteleoperationAIperceptionretrieval-augmentedgenerationsocialrobotsmisidentificationbias
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper asks whether people can tell who is actually driving an embodied robot: a large language model or a human teleoperator. Across two tasks (information assistance and package handover) and two movement modes (static and dynamic), 34 participants completed 136 interactions and then guessed the operator type. The authors claim that accuracy was no better than chance, with 71 of 136 correct (52.21%, binomial p=0.334), and that errors were strongly asymmetric: human operators were mistaken for AI in 50 trials (36.76%) while AI operators were mistaken for humans in only 15 (11.03%). The paper also reports that confidence, age, and chatbot familiarity shaped judgments without rescuing overall accuracy. This matters for the design of social robots and for the wider question of whether the Turing Test transfers to embodied, task-oriented settings.

What carries the argument

The load-bearing object is an embodied Turing Test built on the IVO robot with two matched operator channels. The AI channel uses GPT-4o-mini for dialogue generation plus Retrieval-Augmented Generation (RAG) with LaBSE embeddings grounded in a document about a university building; the human channel uses two remote teleoperators who receive the same document and are instructed to follow it. Both channels feed the same text-to-speech system, and the AI's response timing is artificially delayed to mimic human typing speed. Movement, person tracking, and arm control are shared and triggered by either operator, so the conversational output is the variable under test. The questionnaire, adapted from a prior LLM Turing Test study, asks participants to choose AI or human, rate confidence on a 1-10 scale, and select reasons for their judgment.

What would settle it

Replicate the Turing Test with unscripted human operators who answer from their own knowledge and personality. If participants then identify the operator correctly significantly more often than chance (for example, above 60%), the claim that people cannot tell AI from human operators would be falsified; if the human-misclassified-as-AI asymmetry disappears under unscripted operators, the bias interpretation would also be called into question.

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Extended reading notes

Core claim

The paper's central claim is that in an embodied Turing Test with an LLM-controlled robot, people cannot reliably distinguish AI from human operators. The evidence is a binomial test on 136 judgments: 52.21% correct is not significantly different from 50% ($p=0.334$). The secondary claim is that the two error types are not symmetric: participants misclassified human operators as AI far more often than the reverse, with a chi-square of $\chi^2=34.07$ ($p=5.33\times10^{-9}$). The authors also find that task type and robot movement mode did not affect accuracy, that RAG kept the LLM's factual errors low (8 hallucinations in 64 AI interactions), and that when hallucinations occurred, participants correctly identified the AI in 6 of 8 cases. They interpret the asymmetry as evidence that people associate AI with formality, politeness, and scripted behavior, so human operators who follow guidelines and sound formal are judged to be machines.

Load-bearing premise

The central claim depends on the human operators being representative of natural human interaction, but they were only two people instructed to answer strictly from a document and to 'establish trust,' and the paper itself notes their behavior at times resembled machines; if that baseline is artificial, the result compares two scripted systems rather than AI versus human.

Editorial extensions

If this is right

  • If people cannot tell the operator apart, an embodied LLM robot is already indistinguishable from a human teleoperator in short, task-oriented conversations, including when the robot is moving.
  • The misclassification asymmetry implies the practical risk is over-attribution of AI: human operators acting under guidelines are judged to be machines, which could affect trust in human-operated remote services.
  • Response timing and linguistic formality are the cues that drive judgments, so robot designers who want to be perceived as human should soften formality and vary response delays.
  • RAG appears to keep the LLM factually reliable, while the rare hallucinations expose the AI; further reducing hallucinations would make the AI channel harder to detect.
  • Accuracy did not improve across repeated interactions in the study, suggesting familiarity with the robot by itself does not teach people to spot the AI; training or performance feedback may be needed.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the human baseline is unrepresentative—only two operators, both instructed to stick to a document and to 'establish trust'—then the result may describe how people classify machine-like behavior rather than a genuine AI–human equivalence; a naturalistic human baseline is the next test.
  • The confidence pattern (accuracy rising with confidence for the AI but falling for the human operator) suggests people carry overconfident stereotypes about what AI sounds like; collecting a continuous 'how AI-like is this response' rating before the binary choice would test this directly.
  • Because robot movement mode made no difference, the results predict that conversational content dominates in embodied Turing Tests; an extension that varies physical behavior while holding the dialogue fixed would test that prediction.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper reports an embodied Turing Test with the IVO robot. Thirty-four participants each completed four interactions (two tasks crossed with static and dynamic modes), with operator type as a between-subjects factor: 17 interacted with an LLM (GPT-4o-mini with RAG) and 17 with one of two human teleoperators. After each interaction, participants guessed whether the operator was AI or human. The headline result is that participants identified the operator correctly in 71/136 trials (52.21%), a non-significant binomial test (p=0.334); the paper also reports a strong asymmetry in errors (human operators misclassified as AI 50 times versus AI operators misclassified as human 15 times; chi-square=34.07, p=5.33e-9), alongside analyses of confidence, reasons, demographics, and conversational data. The authors conclude that humans cannot reliably distinguish AI- from human-operated robots beyond chance and tend to over-attribute AI.

Significance. If the central claims are robust, the study is a meaningful extension of LLM Turing Tests from text-only settings to embodied interaction involving navigation and manipulation, and it provides a clear, falsifiable result plus an asymmetry that could inform HRI design. The study has several strengths: it uses a physical robot, two functionally different tasks, three interaction languages, a transparent prompt-design process informed by a pilot study, an explicit response-delay model (Eq. 1), and evaluation by independent participants who did not know the operator assignment. I found no circular-reasoning issue: the pilot-based prompt and delay parameter are imported design choices rather than outcome variables, and the chance-level result is not encoded in the prompt. The significance is currently conditional, however, because the principal statistical claims rest on trial-level tests that ignore the nested data structure.

major comments (3)
  1. [Section IV.A] The headline null result (71/136 correct, binomial p=0.334) treats the 136 judgments as independent trials. The design described in Section III.D has each of 34 participants contributing four judgments, and operator type is constant within a participant; trials are therefore clustered within participants and the operator factor is between-subjects. If accuracy varies across participants, the effective number of independent observations is closer to 34 than 136, and the reported p-value is not valid as a test of the chance-level claim. Please report per-participant accuracy, an intraclass correlation, a mixed-effects logistic regression with participant as a random effect, and/or a participant-level binomial test, or provide raw data so the clustering can be independently assessed. This is load-bearing because the abstract and title state the chance-level result as the central finding.
  2. [Section IV.A] The asymmetry result (50 human-as-AI misidentifications versus 15 AI-as-human, chi-square=34.07, p=5.33e-9) is computed on the aggregate 2x2 table without accounting for repeated measures. Since operator type is between-subjects, the 50 and 15 counts could be driven by a small number of participants who consistently misjudged one operator type. Please report the per-participant distribution of misidentifications (for example, counts of participants making 0-4 errors for each operator type) and provide a clustered test, such as a participant-level Mann-Whitney or Wilcoxon test on the number of misidentifications, or a mixed-effects model, before treating the asymmetry as robust.
  3. [Section III.C.1 and Section V.B] The human baseline is limited to two operators who were instructed to answer only from a provided document in the information task and to 'establish trust' during the handover task. The paper itself acknowledges in Section V.B that these human operators 'exhibited even stronger formal tendencies than the AI' and that when they 'strictly follow the guidelines, their behavior at times resembles that of machines.' As written, the conclusion that people cannot distinguish AI- from human-controlled robots generalizes beyond what the data support; the comparison is between an LLM and a highly constrained teleoperation protocol. Please reframe the central claim to state this boundary condition explicitly, or supplement the baseline with additional, less-constrained operators, and discuss how the null result might differ with a more natural human baseline.
minor comments (5)
  1. [Abstract and general text] The abstract and several other places contain typographical and grammatical errors, including 'associated to the the challenge,' 'system intelligence,' and 'participants responses'; a careful proofread is needed.
  2. [Section III.C.2, Eq. (1)] The notation N(0.3, 0.03) should be explicitly defined as a Gaussian random variable, and the text should state whether the delay is drawn once per response or per character and how negative or near-zero draws are handled.
  3. [Section IV.A] The reported p=0.334 appears to be a one-tailed binomial probability for the direction 'better than chance'; since the text says 'no significant deviation from 50%,' the two-tailed p-value should also be reported for consistency with the wording.
  4. [Section IV.C] The correlation claims involving age and chatbot interaction frequency are based on small subgroups (n_ind=6, 4, 4, 20) and are presented without a correlation coefficient or confidence interval; please report the relevant statistic or use a model that accounts for participant clustering.
  5. [Figure 3] The figure would be easier to interpret if the text specified whether the shaded 95% confidence interval is computed per participant or per trial and how the confidence-level groupings were formed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claim is an empirical result from independent participants and does not reduce by construction to the system's fitted or imported parameters.

full rationale

The paper's central claim—that participants could not distinguish AI- from human-operated robots beyond chance (71/136 correct, binomial p=0.334)—is an empirical outcome measured on independent participant judgments, not a quantity encoded in the experimental setup. The AI operator's prompt was informed by a pilot study of human responses, and the response-delay model (Eq. 1) is an imported design parameter, but neither the prompt nor the delay model enforces the chance-level outcome or the misidentification asymmetry; these are contingent results of the data collection. The human baseline is constrained (two operators following strict guidelines), which the authors acknowledge in Section V.B, but this is a validity threat about the representativeness of the comparison, not a circular reduction of the derivation to its inputs. The few self-citations (e.g., the IVO robot reference [37]) concern the hardware platform and are not load-bearing for the perceptual claim. No fitted parameter is renamed as a prediction, no uniqueness theorem is imported from the authors' prior work, and no known result is merely re-renamed. The statistical critique that trials may not be independent is a methodological concern about the inference, not a circularity in the derivation chain.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central claim depends on three assumptions: the human baseline is representative, the 136 samples are independent, and the speech pipeline is neutral. The paper introduces no new entities and fits no parameters; the delay formula and temperature are hand-set design choices imported from prior work, and they influence perceived cues (response time) but do not determine the outcome.

free parameters (2)
  • response delay scale = 0.3 s/char (N(0.3, 0.03) per character plus 1 s minimum)
    Hand-set in Eq. (1) following [26] to mimic human typing speed; directly affects the AI operator's response timing, which is one of the strongest perceptual cues.
  • LLM temperature = 1
    Chosen for response variability; not reported as optimized.
assumptions (3)
  • domain assumption Human teleoperators following the task instructions produce behavior representative of natural human interaction.
    The Turing Test framework requires a valid human baseline; the paper's own discussion in V.B questions this assumption.
  • domain assumption The 136 interaction samples are independent Bernoulli trials in the binomial test.
    Each participant provides four interactions with the same operator type, so observations are clustered within participants and operator type is constant per participant; independence is asserted without justification in Section IV.A.
  • domain assumption The speech pipeline preserves operator identity cues without systematic bias.
    Both operators use the same voice and TTS (gTTS), so prosodic and vocal identity cues are removed; the paper assumes this creates a fair comparison in Section III.A.

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Cite this review

Pith. "Pith review of AI or Human? Understanding Perceptions of Embodied Robots with LLMs." pith.science (2026). https://pith.science/paper/B65OM2D5

@misc{pith2026250716398,
  author       = {Pith},
  title        = {Pith review of: AI or Human? Understanding Perceptions of Embodied Robots with LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B65OM2D5}},
  note         = {Machine review of arXiv:2507.16398}
}
read the original abstract

The pursuit of artificial intelligence has long been associated to the the challenge of effectively measuring intelligence. Even if the Turing Test was introduced as a means of assessing a system intelligence, its relevance and application within the field of human-robot interaction remain largely underexplored. This study investigates the perception of intelligence in embodied robots by performing a Turing Test within a robotic platform. A total of 34 participants were tasked with distinguishing between AI- and human-operated robots while engaging in two interactive tasks: an information retrieval and a package handover. These tasks assessed the robot perception and navigation abilities under both static and dynamic conditions. Results indicate that participants were unable to reliably differentiate between AI- and human-controlled robots beyond chance levels. Furthermore, analysis of participant responses reveals key factors influencing the perception of artificial versus human intelligence in embodied robotic systems. These findings provide insights into the design of future interactive robots and contribute to the ongoing discourse on intelligence assessment in AI-driven systems.

Figures

Figures reproduced from arXiv: 2507.16398 by the authors.

Figure 1
Figure 1. (a) Participant engaging with the robot in static [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Bar charts comparing the reasons participants attributed to perceiving an AI operator (a) or a human operator (b) [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Scatter plots of the confidence levels in relation to [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Relationships between age, level of chatbot interaction, and accuracy in the Turing Test. The left plot shows a [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Violin plots with the age of the participants and [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

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