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A Service Robot's Guide to Interacting with Busy Customers

Suraj Nukala, Meera Sushma, Leimin Tian, Akansel Cosgun, Dana Kulic

TL;DR

This study investigates how service robots should communicate with cognitively busy customers in hospitality settings. Using a two-part, within-subjects design with a Temi robot, it compares baseline, non-verbal acoustic cues, and a multimodal cue set (visualization, micromotion, and speech) to determine effects on attention capture, order accuracy, and user experience. Part 1 finds non-verbal acoustic cues improve attention but without significant subjective gains; Part 2 shows that visualisation best communicates intent, speech aids delivery, and multimodal cues yield the strongest overall performance, especially in accuracy. The results support deploying multimodal communication strategies to enhance efficiency and user satisfaction in dynamic, real-world service environments, while acknowledging limitations of laboratory settings and the need for real-world validation.

Abstract

The growing use of service robots in hospitality highlights the need to understand how to effectively communicate with pre-occupied customers. This study investigates the efficacy of commonly used communication modalities by service robots, namely, acoustic/speech, visual display, and micromotion gestures in capturing attention and communicating intention with a user in a simulated restaurant scenario. We conducted a two-part user study (N=24) using a Temi robot to simulate delivery tasks, with participants engaged in a typing game (MonkeyType) to emulate a state of busyness. The participants' engagement in the typing game is measured by words per minute (WPM) and typing accuracy. In Part 1, we compared non-verbal acoustic cue versus baseline conditions to assess attention capture during a single-cup delivery task. In Part 2, we evaluated the effectiveness of speech, visual display, micromotion and their multimodal combination in conveying specific intentions (correct cup selection) during a two-cup delivery task. The results indicate that, while speech is highly effective in capturing attention, it is less successful in clearly communicating intention. Participants rated visual as the most effective modality for intention clarity, followed by speech, with micromotion being the lowest ranked.These findings provide insights into optimizing communication strategies for service robots, highlighting the distinct roles of attention capture and intention communication in enhancing user experience in dynamic hospitality settings.

A Service Robot's Guide to Interacting with Busy Customers

TL;DR

This study investigates how service robots should communicate with cognitively busy customers in hospitality settings. Using a two-part, within-subjects design with a Temi robot, it compares baseline, non-verbal acoustic cues, and a multimodal cue set (visualization, micromotion, and speech) to determine effects on attention capture, order accuracy, and user experience. Part 1 finds non-verbal acoustic cues improve attention but without significant subjective gains; Part 2 shows that visualisation best communicates intent, speech aids delivery, and multimodal cues yield the strongest overall performance, especially in accuracy. The results support deploying multimodal communication strategies to enhance efficiency and user satisfaction in dynamic, real-world service environments, while acknowledging limitations of laboratory settings and the need for real-world validation.

Abstract

The growing use of service robots in hospitality highlights the need to understand how to effectively communicate with pre-occupied customers. This study investigates the efficacy of commonly used communication modalities by service robots, namely, acoustic/speech, visual display, and micromotion gestures in capturing attention and communicating intention with a user in a simulated restaurant scenario. We conducted a two-part user study (N=24) using a Temi robot to simulate delivery tasks, with participants engaged in a typing game (MonkeyType) to emulate a state of busyness. The participants' engagement in the typing game is measured by words per minute (WPM) and typing accuracy. In Part 1, we compared non-verbal acoustic cue versus baseline conditions to assess attention capture during a single-cup delivery task. In Part 2, we evaluated the effectiveness of speech, visual display, micromotion and their multimodal combination in conveying specific intentions (correct cup selection) during a two-cup delivery task. The results indicate that, while speech is highly effective in capturing attention, it is less successful in clearly communicating intention. Participants rated visual as the most effective modality for intention clarity, followed by speech, with micromotion being the lowest ranked.These findings provide insights into optimizing communication strategies for service robots, highlighting the distinct roles of attention capture and intention communication in enhancing user experience in dynamic hospitality settings.

Paper Structure

This paper contains 25 sections, 4 figures, 2 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORKS
  3. Visual and Motion-Based Intent Communication
  4. Object Handover Coordination
  5. User Experience and Preferences in HRI
  6. Verbal and Multimodal Intent Disambiguation
  7. METHODOLOGY
  8. Interaction scenarios
  9. Evaluation metrics
  10. Hypotheses
  11. Part 1: Baseline vs non-verbal acoustic cue for attracting user attention
  12. Part 2: Visualisation, Micromotion, Speech, and Multimodal intent communication
  13. RESULTS
  14. Part 1: Baseline vs non-verbal acoustic cues for user attention capturing
  15. H1: The presence of a non-verbal acoustic cue while the robot approaches the user will improve user experience compared to having no cue.
  16. ...and 10 more sections

Figures (4)

  • Figure 1: User study scenario (intention communication stage): the Temi service robot is delivering an order to a "busy" customer, who needs to pick up the correct glass from the two on its tray while playing a typing game.
  • Figure 2: Example visualisation in service intention communication: the user should pick up the cup on the left
  • Figure 3: Illustration of micromotion for service intention communication: after the greeting gesture of slight bowing, the robot rotates to position the cup that the user should pick up to be closer
  • Figure 4: Visualisation comparing Micromotion, Speech, Visualisation, and Multimodal conditions for 3 metrics: Perceived Accuracy [Pickup], Actual Accuracy [Pickup] and Delivery Time