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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.19289 |
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| _version_ | 1866914107706310656 |
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| author | Huang, Kefeng Pipe, Jonathon Martin, Alice E. Wang, Tianyuan Franklin, Barnabas A. Tyrrell, Andy M. Fairlamb, Ian J. S. Zhu, Jihong |
| author_facet | Huang, Kefeng Pipe, Jonathon Martin, Alice E. Wang, Tianyuan Franklin, Barnabas A. Tyrrell, Andy M. Fairlamb, Ian J. S. Zhu, Jihong |
| contents | Chemistry laboratory automation aims to increase throughput, reproducibility, and safety, yet many existing systems still depend on frequent human intervention. Advances in robotics have reduced this dependency, but without a structured representation of the required skills, autonomy remains limited to bespoke, task-specific solutions with little capacity to transfer beyond their initial design. Current experiment abstractions typically describe protocol-level steps without specifying the robotic actions needed to execute them. This highlights the lack of a systematic account of the manipulation skills required for robots in chemistry laboratories. To address this gap, we introduce TARMAC - a Taxonomy for Robot Manipulation in Chemistry - a domain-specific framework that defines and organizes the core manipulations needed in laboratory practice. Based on annotated teaching-lab demonstrations and supported by experimental validation, TARMAC categorizes actions according to their functional role and physical execution requirements. Beyond serving as a descriptive vocabulary, TARMAC can be instantiated as robot-executable primitives and composed into higher-level macros, enabling skill reuse and supporting scalable integration into long-horizon workflows. These contributions provide a structured foundation for more flexible and autonomous laboratory automation. More information is available at https://tarmac-paper.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19289 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TARMAC: A Taxonomy for Robot Manipulation in Chemistry Huang, Kefeng Pipe, Jonathon Martin, Alice E. Wang, Tianyuan Franklin, Barnabas A. Tyrrell, Andy M. Fairlamb, Ian J. S. Zhu, Jihong Robotics Chemistry laboratory automation aims to increase throughput, reproducibility, and safety, yet many existing systems still depend on frequent human intervention. Advances in robotics have reduced this dependency, but without a structured representation of the required skills, autonomy remains limited to bespoke, task-specific solutions with little capacity to transfer beyond their initial design. Current experiment abstractions typically describe protocol-level steps without specifying the robotic actions needed to execute them. This highlights the lack of a systematic account of the manipulation skills required for robots in chemistry laboratories. To address this gap, we introduce TARMAC - a Taxonomy for Robot Manipulation in Chemistry - a domain-specific framework that defines and organizes the core manipulations needed in laboratory practice. Based on annotated teaching-lab demonstrations and supported by experimental validation, TARMAC categorizes actions according to their functional role and physical execution requirements. Beyond serving as a descriptive vocabulary, TARMAC can be instantiated as robot-executable primitives and composed into higher-level macros, enabling skill reuse and supporting scalable integration into long-horizon workflows. These contributions provide a structured foundation for more flexible and autonomous laboratory automation. More information is available at https://tarmac-paper.github.io/ |
| title | TARMAC: A Taxonomy for Robot Manipulation in Chemistry |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.19289 |