Measuring progress in robotics: benchmarking and the ‘measure-target confusion’

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Abstract

While it is often said that in order to qualify as a true science robotics should aspire to reproducible and measurable results that allow benchmarking, I argue that a focus on benchmarking will be a hindrance for progress. Several academic disciplines that have been led into pursuing only reproducible and measurable ‘scientific’ results—robotics should be careful not to fall into that trap. Results that can be benchmarked must be specific and context-dependent, but robotics targets whole complex systems independently of a specific context—so working towards progress on the technical measure risks missing that target. It would constitute aiming for the measure rather than the target: what I call ‘measure-target confusion’. The role of benchmarking in robotics shows that the more general problem to measure progress towards more intelligent machines will not be solved by technical benchmarks; we need a balanced approach with technical benchmarks, real-life testing and qualitative judgment.

Original languageEnglish
Title of host publicationMetrics of Sensory Motor Coordination and Integration in Robots and Animals
Subtitle of host publicationHow to Measure the Success of Bioinspired Solutions with Respect to their Natural Models, and Against More ‘Artificial’ Solutions?
EditorsFabio Bonsignorio, Elena Messina, Angel P. del Pobil, John Hallam
Place of PublicationCham
PublisherSpringer
Pages169-179
Number of pages11
ISBN (Electronic)978-3-030-14126-4
ISBN (Print)978-3-030-14124-0
DOIs
Publication statusPublished - 1 Jan 2020

Publication series

NameCognitive Systems Monographs
Volume36
ISSN (Print)1867-4925
ISSN (Electronic)1867-4933

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Müller, V. C. (2020). Measuring progress in robotics: benchmarking and the ‘measure-target confusion’. In F. Bonsignorio, E. Messina, A. P. del Pobil, & J. Hallam (Eds.), Metrics of Sensory Motor Coordination and Integration in Robots and Animals: How to Measure the Success of Bioinspired Solutions with Respect to their Natural Models, and Against More ‘Artificial’ Solutions? (pp. 169-179). (Cognitive Systems Monographs; Vol. 36). Cham: Springer. https://doi.org/10.1007/978-3-030-14126-4_9