Expressing Robot Incapability

Abstract

Our goal is to enable robots to express their incapability, and to do so in a way that communicates both what they are trying to accomplish and why they are unable to accomplish it. We frame this as a trajectory optimization problem: maximize the similarity between the motion expressing incapability and what would amount to successful task execution, while obeying the physical limits of the robot. We introduce and evaluate candidate similarity measures, and show that one in particular generalizes to a range of tasks, while producing expressive motions that are tailored to each task. Our user study supports that our approach automatically generates motions expressing incapability that communicate both what and why to end-users, and improve their overall perception of the robot and willingness to collaborate with it in the future.

HRI 2018

References in corpus (3)

• Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaboration
• Expressive Robot Motion Timing
• Exploring Implicit Human Responses to Robot Mistakes in a Learning from Demonstration Task

Cited by in corpus (8)

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• Generative Expressive Robot Behaviors using Large Language Models
• Is it personal? The impact of personally relevant robotic failures (PeRFs) on humans' trust, likeability, and willingness to use the robot
• Aligning Robot and Human Representations
• From Agent Autonomy to Casual Collaboration: A Design Investigation on Help-Seeking Urban Robots
• Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration
• Signaling Human Intentions to Service Robots: Understanding the Use of Social Cues during In-Person Conversations