Designing effective Human-Robot Interaction in task-oriented settings requires carefully balancing user engagement with socially acceptable levels of robot intrusiveness. In this paper, we examine how different robot intervention strategies shape user experience, interaction dynamics, perceived intrusiveness, and sense of support. We compare two approaches: a continuous engagement-seeking robot strategy, and a context-aware strategy that selectively intervenes based on the user's state and task context. Both approaches rely on multimodal behavioral cues, including body orientation and attentional signals, to guide robot actions. We evaluate these strategies in a user study with 32 participants performing a task in a simulated hospital environment. Our findings show that higher interaction frequency does not necessarily lead to better engagement. Instead, we observe a systematic trade-off between perceived support and intrusiveness, influenced by factors such as physical proximity and user effort. These results provide empirical evidence that effective engagement in HRI depends on adaptive, context-sensitive intervention policies.
Figures & tables
Fig. 1 : Context-aware robot behavior. Top : Participant focused on the task, showing no intention to interact with the robot. Bottom : RViz visualization of the robot’s perception.
Fig. 2 : Experimental setup. Top : Participant facing the robot, indicating an intention to interact. Bottom : RViz visualization of the robot’s perception. The yellow bounding box marks the person, while the red and blue arrows indicate the torso and head directions, respectively.
Fig. 3 : Proposed systems. Left : continuous engagement system. Right : balanced controller system. Both systems use visual input to track the person. In System 1, a continuous controller drives motion and dialogue, with the LLM invoked only under specific conditions. In System 2, the LLM controls responses and motion based on user input and visual context, with a balanced controller triggering occasional interventions when no audio input is detected.
Fig. 4 : Robot and task items. (a) Robot screen showing listening (green) and responding (blue) states, along with dialog transcripts; a red skip button interrupts responses and resumes listening. (b) Food, (c) Fluids, (d) Medication pills.
System
S1
S2
Duration (s)
303.2±91.5
342.3±113.3
Position changes
7.6±3.1
11.3±5.5
User Inputs
18.3±9.9
21.8±10.4
System Outputs
28.1±10.5
25.8±11.5
TABLE I : Experiment metrics . Average values measured.
Fig. 5 : Correlations between objective and subjective metrics. Scatter plots showcasing correlation for both systems, grouped by objective metric. Dashed lines and solid lines indicate non-significant and significant correlations ( p<0.05 ), respectively. Legend with statistics’ values: Valence Score (VS), Arousal Score (AS), Reward Factor (RF), Perceived Intelligence (PI), and Perceived Usability (PU).
Robot assistance is particularly crucial in unfamiliar tasks, where users must understand task requirements while coordinating with the robot. Previous research offers mixed evidence on the role of robot proxemics in user engagement: some studies suggest closer proximity enhances interaction, while others report it can feel intrusive. In this work, we argue that perceptions of intrusiveness depend not only on proxemics but also on the frequency of robot interventions, and are strongly influenced by individual traits such as personality and demographics. We conducted an experiment with 32 participants who interacted with two assistive robots that provided similar task support but differed in their intervention strategies. Results indicate that overall engagement remains stable across conditions, yet affective responses and perceived intrusiveness vary significantly with personality traits. Moreover, personality shapes interaction dynamics differently depending on the robot's behavior. These findings emphasize that effective human-robot interaction should account for individual differences, tailoring robot behavior to maintain engagement while respecting each user's unique affective and behavioral profile.
Valerio Bo, Lavinia Hriscu, Alberto Sanfeliu +1
Institut de Robòtica i Informàtica Industrial (CSIC-UPC) · Universitat Politècnica de Catalunya (UPC), Barcelona, 08034, Spain.
Conventional human-in-the-loop approaches typically involve users only when a robot encounters failure or uncertainty, treating humans primarily as tools for improving robot performance. However, in many human-centered robotics settings, interaction should support engagement by keeping users involved in decision-making rather than limiting them to failure-driven interventions. This is particularly compelling in physical caregiving, where mobility limitations can reduce users' ability to intervene or modulate the robot's behavior in the moment. As a result, failure-driven interaction policies may relegate users to passive observers for long stretches of the task. For example, a user with mobility limitations may feel less engaged when being continuously and passively fed by a robot. At the same time, overly frequent interaction can be tiring and increase the user's workload. To address this trade-off, we propose Engagement-aware MPC (E-MPC), a user-engagement-aware method that plans interaction to maintain engagement while respecting a workload constraint. E-MPC leverages a user interaction dynamics model that captures how user engagement evolves as a function of both the frequency and type of interaction. Rather than requesting input only when difficulties arise during task execution, the robot proactively considers the user's preferred level of engagement throughout the task, balancing autonomy and interaction while ensuring task success. We evaluate E-MPC in simulation with several ablations and baseline comparisons. Results demonstrate the effectiveness of our approach across diverse user personas. In addition, we conduct a real-world user study with participants with emulated mobility limitations on a robot-assisted bite acquisition system, showing that E-MPC improves user experience while maintaining task success.
This paper presents an experimental design for constructing a multimodal dataset to analyze user engagement in human-robot interaction (HRI). Prior studies have mainly relied on observable behavioral cues, with limited frameworks integrating physiological signals. We therefore propose a structured data-collection protocol to build a multimodal dataset that includes wearable physiological signals, behavioral data, and self-report measures under different levels of task complexity defined in this experiment.
Buwan Kim, Wonse Jo
Department of Information and Telecommunication Engineering, Incheon National University, Incheon, South Korea 22012.