Designing for Error Recovery in Human-Robot Interaction

2026-04-14

AI Summary

This work addresses a critical limitation in current robotic AI systems, which often prioritize surpassing human performance in single-task scenarios while lacking the capacity to detect and recover from errors during continuous human–robot interaction. Focusing on nuclear glovebox operations as a high-stakes application domain, the study proposes a novel robotic system architecture explicitly designed around error recovery. Integrating principles from human factors engineering and interactive AI, the framework incorporates mechanisms for real-time error detection, feedback loops, and adaptive adjustment to enhance robustness and adaptability in dynamic, real-world environments. By centering system design on resilience and learning from mistakes, this approach establishes a new paradigm for fault-tolerant and adaptive human–robot collaboration in safety-critical settings.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This position paper looks briefly at the way we attempt to program robotic AI systems. Many AI systems are based on the idea of trying to improve the performance of one individual system to beyond so-called human baselines. However, these systems often look at one shot and one-way decisions, whereas the real world is more continuous and interactive. Humans, however, are often able to recover from and learn from errors - enabling a much higher rate of success. We look at the challenges of building a system that can detect/recover from its own errors, using the example of robotic nuclear gloveboxes as a use case to help illustrate examples. We then go on to talk about simple starting designs.

Problem

• error recovery
• human-robot interaction
• robotic AI systems
• interactive systems
• error detection

Innovation

• error recovery
• human-robot interaction
• adaptive AI
• interactive robotics
• resilient systems