RoboCafé improves robot interactions over many days in public spaces
RoboCafé in the Open: Interaction Continuity in Long-Term Public Human-Robot Interaction
Robotics
Summary
Robots in public places need to remember who they are talking to and what happened before in order to have smooth conversations over time. The authors created RoboCafé, a coffee robot that talks to people, remembers past orders, and notices who is nearby. They tested RoboCafé for 12 days at a university and found it had to carefully figure out who was present and which conversation was happening to keep interactions going well. Based on this, the authors describe four important features robots need for long-lasting public talks.
What this means in practice
- •For public space robot developers: Build robots for places like cafes or campuses that can remember and recognize repeat customers for smoother conversations over many days.
- •For customer service automation teams: Create service robots that manage continuous interaction state and person tracking for better customer experiences in busy, changing environments.
Authors
Kaitlynn Taylor Pineda, Kush Kumar Kushwaha, Jie Wang, Jiaming Du, Anvii Mishra, Emilie Basu Suri, Angela Guo, Chien-Ming Huang
Abstract
As robots remain in public spaces over extended periods, they must maintain interaction continuity by preserving and correctly applying context as people, encounters, and circumstances change. To study interaction continuity in long-term public human-robot interactions, we developed RoboCafé, an autonomous conversational coffee robot designed to support repeated interactions through task-aware dialogue, real-time multimodal perception, and memory of prior encounters. We deployed RoboCafé for 12 days in a university building, where it received 148 orders. The deployment involved repeat customers, passersby, changing groups, and back-to-back orders that repeatedly crossed the boundaries assumed by the system's order-centered interaction model. We found that successful interaction continuity requires a robot to determine who is currently present, which prior context belongs to whom, where interactions begin and end, and whether its representation of an interaction matches what is occurring in the physical world. From these observations, we derive four system design requirements for maintaining interaction continuity in longitudinal public human-robot interactions: contextual interaction state, persistent person grounding, explicit interaction life-cycle management, and interaction observability.