Faulty Coffees: Barriers to Adoption of an In-the-wild Robo-Barista

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.

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.

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.