Designing for Error Recovery in Human-Robot Interaction
Christopher D. Wallbridge, Erwin Jose Lopez Pulgarin
TLDR
This paper proposes designing robotic AI systems for error recovery, drawing inspiration from human learning and adaptation in continuous, interactive environments.
Key contributions
- Critiques current AI systems for their one-shot, one-way decision-making in complex, interactive environments.
- Advocates for designing robotic AI that can detect, recover from, and learn from its own errors, like humans.
- Discusses challenges in implementing robust error recovery mechanisms in human-robot interaction.
- Presents robotic nuclear gloveboxes as a specific use case to illustrate error recovery designs.
Why it matters
This paper shifts focus from perfect performance to resilient interaction, crucial for real-world robotic deployment. By emphasizing error recovery, it addresses a fundamental limitation of current AI, paving the way for more robust and adaptable human-robot systems.
Original 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.
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