Categorizing robots by performance fitness into the tree of robots

Abstract Robots are typically classified based on specific morphological features, like their kinematic structure. However, a complex interplay between morphology and intelligence shapes how well a robot performs processes. Just as delicate surgical procedures demand high dexterity and tactile precision, manual warehouse or construction work requires strength and endurance. These process requirements necessitate robot systems that provide a level of performance fitting the process. In this work, we introduce the tree of robots as a taxonomy to bridge the gap between morphological classification and process-based performance. It classifies robots based on their fitness to perform, for example, physical interaction processes. Using 11 industrial manipulators, we constructed the first part of the tree of robots based on a carefully deduced set of metrics reflecting fundamental robot capabilities for various industrial physical interaction processes. Through significance analysis, we identified substantial differences between the systems, grouping them via an expectation-maximization algorithm to create a fitness-based robot classification that is open for contributions and accessible.

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Institutions

Publication Details

Journal
Nature Machine Intelligence
Published
2025-02-21
DOI
https://doi.org/10.1038/s42256-025-00995-y
Citations
4
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
4.86

Funders

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article

Categorizing robots by performance fitness into the tree of robots

Sami Haddadin, Kübra Karacan, Robin Jeanne Kirschner, Alessandro Melone
4 citations
Nature Machine Intelligence
Robot Manipulation and Learning
4.86
article

Categorizing robots by performance fitness into the tree of robots

Sami Haddadin, Kübra Karacan, Robin Jeanne Kirschner, Alessandro Melone
article en
4 citations

Abstract

Abstract Robots are typically classified based on specific morphological features, like their kinematic structure. However, a complex interplay between morphology and intelligence shapes how well a robot performs processes. Just as delicate surgical procedures demand high dexterity and tactile precision, manual warehouse or construction work requires strength and endurance. These process requirements necessitate robot systems that provide a level of performance fitting the process. In this work, we introduce the tree of robots as a taxonomy to bridge the gap between morphological classification and process-based performance. It classifies robots based on their fitness to perform, for example, physical interaction processes. Using 11 industrial manipulators, we constructed the first part of the tree of robots based on a carefully deduced set of metrics reflecting fundamental robot capabilities for various industrial physical interaction processes. Through significance analysis, we identified substantial differences between the systems, grouping them via an expectation-maximization algorithm to create a fitness-based robot classification that is open for contributions and accessible.

Nature Machine IntelligenceVol. 7(3)
Mohamed bin Zayed University of Artificial Intelligence (AE), Technical University of Munich (DE)
European Commission, Doosan Heavy Industries and Construction, Technische Universität München
Openalex Percentile: Top 6%
Robot Manipulation and Learning
4.86
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Categorizing robots by performance fitness into the tree of robots — Sami Haddadin, Kübra Karacan, et al. · Nature Machine Intelligence (2025) | TGRS Research Map | TGRS