Trust in Autonomous Human--Robot Collaboration: Effects of Responsive Interaction Policies

Shauna Heron; Meng Cheng Lau

cs.ROFeb 25, 2026

Trust in Autonomous Human--Robot Collaboration: Effects of Responsive Interaction Policies

Authors: Shauna Heron, Meng Cheng Lau

Organizations: School of Social Sciences, Laurentian University, Sudbury, ON, P3E 2C6 · School of Engineering and Computer Science, Laurentian University, Sudbury, ON, P3E 2C6

Abstract

Trust plays a central role in human--robot collaboration, yet its formation is rarely examined under the constraints of fully autonomous interaction. This pilot study investigated how interaction policy influences trust during in-person collaboration with a social robot operating without Wizard-of-Oz control or scripted repair. Participants completed a multi-stage collaborative task with a mobile robot that autonomously managed spoken-language dialogue, affect inference, and task progression. Two interaction policies were compared: a responsive policy, in which the robot proactively adapted its dialogue and assistance based on inferred interaction state, and a neutral, reactive policy, in which the robot provided only direct, task-relevant responses when prompted. Responsive interaction was associated with significantly higher post-interaction trust under viable communication conditions, despite no reliable differences in overall task accuracy. Sensitivity analyses indicated that affective and experiential components of trust were more sensitive to communication breakdown than evaluative judgments of reliability, and that as language-mediated interaction degraded, the trust advantage associated with responsiveness attenuated and ratings became less clearly interpretable as calibrated evaluations of collaborative competence. These findings suggest that trust in autonomous human--robot interaction emerges from process-level interaction dynamics and operates within constraints imposed by communication viability, highlighting the importance of evaluating trust under real autonomy conditions when designing interactive robotic systems.

Figures & tables

Figure 1: Experimental setup showing the autonomous robot and participant-facing task interface used during in-person sessions. Participants entered task responses and navigated between task stages using the interface, while the robot autonomously tracked task state and adapted its interaction based on participant input.

Figure 2: Task 1 interface including the 6 $×$ 4 grid of 24 candidates. Participants could track those eliminated by clicking on subjects which would grey them out. A box was provided to input their final answer and a button included to move to the next task.

Characteristic	N	CONTROL N = 13 1	RESPONSIVE N = 16 1	p-value 2
Gender	27			0.84
Woman		6 / 13 (46%)	7 / 14 (50%)
Man		7 / 13 (54%)	7 / 14 (50%)
Age Group	27			0.35
18-24		5 / 13 (38%)	7 / 14 (50%)
25-34		4 / 13 (31%)	2 / 14 (14%)

Table 1: Baseline participant characteristics by interaction policy (full sample)

Characteristic	CONTROL N = 10 1	RESPONSIVE N = 14 1	p-value 2
Experienced Trust (TI-HRC)	39 (22)	67 (21)	0.004
Subscales
Reliability	40 (24)	65 (18)	0.012
Trust Perception	42 (23)	60 (22)	0.075
Affective Trust	50 (31)	79 (22)	0.018
Perceived Trust (TPS-HRI)	59 (17)	77 (18)	0.022

Table 2: Post-Interaction Trust and Task Accuracy (eligible sample)

Figure 3: Distribution of Trust Perception by interaction policy. Points represent individual observations; violins depict score distributions. Red points indicate group means with 95% confidence intervals. Statistical comparisons are reported in the Results section.

Characteristic	CONTROL N = 10 1	RESPONSIVE N = 14 1	p-value 2
Objective Measure Averages
Dialogue Turns	34 (9)	33 (5)	0.45
Session Duration (min)	13.24 (3.06)	15.26 (2.12)	0.084
Response Time (ms)	14.37 (3.76)	17.24 (2.52)	<0.001
Silent Periods	5.60 (1.96)	4.71 (2.05)	0.29
Engaged Responses	2.00 (2.21)	3.50 (1.95)	0.040

Table 3: Post-Interaction Objective Measure Averages by Policy in the Eligible Sample

Figure 4: Posterior distributions of fixed effects from the final Bayesian mixed-effects models (eligible sample: n=24) predicting post-interaction trust. Fixed effects include policy, Negative Attitudes Towards Robots (NARS) and language (binary). Half-eye densities show posterior distributions; points indicate posterior medians; thick and thin intervals denote 80% and 95% credible intervals, respectively. The dashed vertical line marks a null effect, and positive values indicate higher trust scores on the respective scale (points on a 0-100 scale).

Figure 5: Predicted trust as a function of communication breakdown by interaction policy. Panels show experienced trust (left) and perceived trust (right). Lines represent posterior medians and shaded bands indicate 80% credible intervals. Communication breakdown is defined as the proportion of interaction turns flagged as breakdowns; predictions are shown at mean baseline negative attitudes toward robots.

Characteristic	CONTROL N = 10 1	RESPONSIVE N = 14 1	p-value 2
% Task Accuracy	0.60 (0.21)	0.66 (0.23)	0.47
Suspect ID Accuracy (robot dependent)	3 / 10 (30%)	9 / 14 (64%)	0.10
Building ID Accuracy	7 / 10 (70%)	11 / 14 (79%)	0.67
Zone ID Accuracy	5 / 10 (50%)	4 / 14 (29%)	0.40
Floor ID Accuracy	7 / 10 (70%)	13 / 14 (93%)	0.27

Table 4: Post-Interaction Task Performance by Policy in the Eligible Sample

Figure 6: Correlation Matrix of Key Study Variables

Figure 7: A selection of facial expressions the language model could choose from each turn. Retrieved from [1] N. T. White, B. Cagiltay, J. E. Michaelis, and B. Mutlu, “Designing Emotionally Expressive Social Commentary to Facilitate Child-Robot Interaction,” in Interaction Design and Children, Athens Greece: ACM, Jun. 2021, pp. 314–325. doi: 10.1145/3459990.3460714.

Variable	Type	Description
task_outcome	categorical	Final task status ( completed , timeout , skipped , partial , abandoned ).
task_completed_without_help	binary	Task was completed without any help requests to the robot.

Table 12

Variable	Type	Description
human_help_request	binary	Participant explicitly or implicitly asks the robot for help or guidance.
human_reasoning	binary	Participant reasons out loud with the robot toward problem-solving.
human_confirmation_seeking	binary	Participant seeks confirmation of a tentative belief or solution.
human_sentence_fragment	binary	Unintelligable sentence fragment

Table 13

Variable	Type	Description
robot_helpful_guidance	binary	Robot provides accurate, task-relevant information or guidance.
robot_unhelpful	binary	Robot provides misleading or incorrect guidance.
robot_stt_failure	binary	Robot response reflects a speech-to-text or input understanding failure.
robot_clarification_request	binary	Robot asks the participant for information or to repeat or clarify their input.
robot_reasoning	binary	Robot reasons out loud with the participant to assist problem-solving

Table 14

Variable	Type	Description
robot_empathy_expression	binary	Robot expresses empathy, encouragement, or reassurance.
robot_emotion_acknowledgement	binary	Robot explicitly references an inferred participant emotional state.
robot_collaborative_language	binary	Robot uses language like “we”, “lets”, “together”.

Table 15

Variable	Type	Description
human_affective_engagement	binary	Participant responds in a socially warm or engaged manner (e.g., “It’s up to us, Misty!” and/or mirrors or responds to the robot’s affective expression and/or treats the robot as a social agent.

Table 16

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Agent-Based Simulation of Trust Development in Human-Robot Teams: An Empirically-Validated Framework

Ravi Kalluri

College of Professional Studies, Northeastern University – San Jose, California, United States

Human-Robot Collaboration Human-Robot Interaction Trust in AI Agent-Based Modeling

Open paper AskPDF arXiv

This paper presents an empirically grounded agent-based model capturing trust dynamics, workload distribution, and collaborative performance in human-robot teams. The model, implemented in NetLogo 6.4.0, simulates teams of 2--10 agents performing tasks of varying complexity. We validate against Hancock et al.'s (2021) meta-analysis, achieving interval validity for 4 of 8 trust antecedent categories and strong ordinal validity (Spearman \r{ho}=0.833ρ= 0.833 \r{ho}=0.833). Sensitivity analysis using OFAT and full factorial designs (n=50n = 50 n=50 replications per condition) reveals robot reliability exhibits the strongest effect on trust (η2=0.35η^2 = 0.35 η2=0.35) and dominates task success (η2=0.93η^2 = 0.93 η2=0.93) and productivity (η2=0.89η^2 = 0.89 η2=0.89), consistent with meta-analytic findings. Trust asymmetry ratios ranged from 0.07 to 0.55 -- below the meta-analytic benchmark of 1.50 -- revealing that per-event asymmetry does not guarantee cumulative asymmetry when trust repair mechanisms remain active. Scenario analysis uncovered trust-performance decoupling: the Trust Recovery scenario achieved the highest productivity (4.29) despite the lowest trust (38.2), while the Unreliable Robot scenario produced the highest trust (73.2) despite the lowest task success (33.4%), establishing calibration error as a critical diagnostic distinct from trust magnitude. Factorial ANOVA confirmed significant main effects for reliability, transparency, communication, and collaboration (p<.001p < .001 p<.001), explaining 45.4% of trust variance. The open-source implementation provides an evidence-based tool for identifying overtrust and undertrust conditions prior to deployment.

Abstract

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