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About this paper
Quazi Marufur Rahman, Peter Corke, Feras Dayoub
This survey paper reviews emerging trends in run-time monitoring for machine learning-based robotic perception, classifying methods that enable robots to assess their perception system's performance and reliability post-deployment when encountering novel data. It categorizes approaches into those based on past experiences, inference inconsistencies, and uncertainty estimation.
Key insights
- Run-time monitoring is critical for enhancing the safety and trustworthiness of autonomous robots, as it complements design-time efforts by enabling performance assessment in dynamic, unpredictable operational environments.
- Diverse methodological approaches are being developed to detect perception failures or quantify reliability at run-time, utilizing strategies from learning explicit failure predictors to analyzing internal model states or intrinsic uncertainty measures.
- A holistic approach, often combining multiple monitoring locations within the perception pipeline and integrating various techniques, appears promising for robust run-time performance assessment.
Summary by alphaXiv.
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