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This paper introduces TARCAT, a comprehensive taxonomy of construction task activities derived from an analysis of 91 O*NET tasks and 30 instructional videos, aimed at enhancing the capabilities of robot workers in construction. By defining 41 action primitives organized into 12 groups and three classes, TARCAT facilitates the composition of parameterized sequences into reusable skills, which can be utilized for robot programming and skill retrieval. The implementation of selected primitives on a DOBOT CR3 arm illustrates the practical application of this taxonomy in developing versatile construction robots that can better understand and execute human-like tasks.
TARCAT offers a structured vocabulary that transforms how robots can learn and execute complex construction tasks, bridging the gap between human work and robotic capabilities.
Recent vision-language-action models offer a path toward robots with broader repertoires than conventional task-specific systems. Construction deployment, however, requires a precise inventory of worker activities and the capabilities needed to execute them. We present TARCAT, an occupation-grounded taxonomy derived from 91 O*NET tasks across seven high-employment construction occupations and 30 instructional videos of physical work. TARCAT defines 41 action primitives in 12 groups and three classes and provides a mechanism for composing parameterized primitive sequences into reusable skills. This human-interpretable structure can organize demonstrations, specify robot requirements, and support coding agents that retrieve and extend skill libraries. We also demonstrate selected primitives on a DOBOT CR3 arm with a CRAFT hand. TARCAT thereby provides a common vocabulary for analyzing human work and developing general-purpose construction robots. Annotations are available at https://github.com/AICPS/TARCAT-Taxonomy.
