The Dilemma of Decision-Making in the Real World: When Robots Struggle to Make Choices Due to Situational Constraints
Khairidine Benali, Praminda Caleb-Solly
TL;DR
The paper tackles decision-making under uncertainty in real-world assistive robotics, focusing on how noise, disabilities, and daily living activities create challenges for safe and intuitive human–robot collaboration. It introduces a Scenario Analysis framework to study user and environmental variability and to guide user studies and the design of improved robots in terms of embodiment, sensing, actuation, and cognition. By integrating STPA, SHARD-UML, and related hazard-analysis methods, the work identifies potential failure modes and offers actionable guidelines for robust, multimodal human–robot interaction, supplemented by real-world challenge videos. The contributions promote personalized collaboration, resilient multi-modal interfaces, and user-centered design to enhance reliability, safety, and adoption of assistive robots in complex environments.
Abstract
In order to demonstrate the limitations of assistive robotic capabilities in noisy real-world environments, we propose a Decision-Making Scenario analysis approach that examines the challenges due to user and environmental uncertainty, and incorporates these into user studies. The scenarios highlight how personalization can be achieved through more human-robot collaboration, particularly in relation to individuals with visual, physical, cognitive, auditory impairments, clinical needs, environmental factors (noise, light levels, clutter), and daily living activities. Our goal is for this contribution to prompt reflection and aid in the design of improved robots (embodiment, sensors, actuation, cognition) and their behavior, and we aim to introduces a groundbreaking strategy to enhance human-robot collaboration, addressing the complexities of decision-making under uncertainty through a Scenario analysis approach. By emphasizing user-centered design principles and offering actionable solutions to real-world challenges, this work aims to identify key decision-making challenges and propose potential solutions.