Beyond Failure Recovery: An Engagement-Aware Human-in-the-loop Framework for Robotic Systems

• URL: http://arxiv.org/abs/2606.18189v1
• Date: Tue, 16 Jun 2026 17:21:43 GMT
• Title: Beyond Failure Recovery: An Engagement-Aware Human-in-the-loop Framework for Robotic Systems
• Abstract summary: In many human-centered robotics settings, interaction should support engagement by keeping users involved in decision-making.<n>In physical caregiving, mobility limitations can reduce users' ability to intervene or modulate the robot's behavior in the moment.<n>We propose Engagement-aware MPC (E-MPC), a user-engagement-aware method that plans interaction to maintain engagement while respecting a workload constraint.
• Score: 2.4077530504640205
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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