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Robotic telepresence enables users to navigate and experience remote environments. However, effective navigation and situational awareness depend on users’ prior knowledge of the environment, limiting the usefulness of these systems for exploring unfamiliar places. We explore how integrating location-aware LLM-based narrative capabilities into a mobile robot can support remote exploration. We developed a prototype system, called NarraGuide, that provides narrative guidance for users to explore and learn about a remote place through a dialogue-based interface. We deployed our prototype in a geology museum, where remote participants (𝑛 = 20) used the robot to tour the museum. Our findings reveal how users perceived the robot’s role, engaged in dialogue in the tour, and expressed preferences for bystander encountering. Our work demonstrates the potential of LLM-enabled robotic capabilities to deliver location-aware narrative guidance and enrich the experience of exploring remote environments.
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System Integration of a Tour Guide Robot
In today’s world, people visit many attractive places. On such an occasion, It is of utmost importance to be accompanied by a tour guide, who is known to explain about the cultural and historical importance of places. Therefore, a human guide is necessary to provide tours for a group of visitors. However, Human tour guides might face tiredness, distraction, and the effects of repetitive tasks while providing tour service to visitors. Robots eliminate these problems and can provide tour consistently until it drains its battery. This experiment introduces a tour-guide robot that can navigate autonomously in a known map of a given place and at the same time interact with people. The environment is equipped with artificial landmarks. Each landmark provides information about that specific region. An Animated avatar is simulated on the screen. IBM Watson provides voice recognition and text-to-speech services for human-robot interaction. The experimental results show that the robot takes average time of 10000 seconds to provide a tour. TEB and DWA local planner are compared by allowing the robot to autonomously maneuver the environment for 9 trials which is tabulated in section V.
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- Award ID(s):
- 2125362
- PAR ID:
- 10343333
- Date Published:
- Journal Name:
- 2022 17th Annual System of Systems Engineering Conference (SOSE)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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