Towards a Verifiable Toolchain for Robotics

There is a growing need for autonomous robots to complete complex tasks robustly in dynamic and unstructured environments. However, current robot performance is limited to simple tasks in controlled environments. To improve robot autonomy in complex environments, the robot's deliberation system must be able to synthesise correct plans for a task and generate contingency plans for handling anomalous scenarios that were not expected at design time. The robustness of such a system can be quantified using techniques for formal verification and validation. This paper outlines the progress of EU project CONVINCE (CONtext-aware Verifiable and adaptIve dyNamiC dEliberation), which aims to develop a software toolchain that aids developers in designing, developing, and deploying robot deliberation systems that are fully verified. We describe our modelling approach, each of the toolchain components, and how they interact. We also discuss survey results which demonstrate the demand for a verifiable toolchain among the robotics community.

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Publication Details

Journal
Proceedings of the AAAI Symposium Series
Published
2024-11-08
DOI
https://doi.org/10.1609/aaaiss.v4i1.31823
Citations
3
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
0.98

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article

Towards a Verifiable Toolchain for Robotics

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3 citations
Proceedings of the AAAI Symposium Series
Robot Manipulation and Learning
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Towards a Verifiable Toolchain for Robotics

Ralph Lange, Lorenzo Natale, Stefano Bernagozzi, Marco Lampacrescia, Yazz Warsame, Michaela Klauck, Christian Henkel, Enrico Ghiorzi, Danielle Wallis, Matteo Morelli, Marco Randazzo, Raphaël Lallement, Masoumeh Mansouri, Stefano Rosa, Charlie Street, Armando Tacchella, S. Faraci, G. Chen, Razane Azrou, Matteo Palmas
article en
3 citations

Abstract

There is a growing need for autonomous robots to complete complex tasks robustly in dynamic and unstructured environments. However, current robot performance is limited to simple tasks in controlled environments. To improve robot autonomy in complex environments, the robot's deliberation system must be able to synthesise correct plans for a task and generate contingency plans for handling anomalous scenarios that were not expected at design time. The robustness of such a system can be quantified using techniques for formal verification and validation. This paper outlines the progress of EU project CONVINCE (CONtext-aware Verifiable and adaptIve dyNamiC dEliberation), which aims to develop a software toolchain that aids developers in designing, developing, and deploying robot deliberation systems that are fully verified. We describe our modelling approach, each of the toolchain components, and how they interact. We also discuss survey results which demonstrate the demand for a verifiable toolchain among the robotics community.

Proceedings of the AAAI Symposium SeriesVol. 4(1)
Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Italian Institute of Technology (IT), Laboratoire d'Intégration des Systèmes et des Technologies (FR), University of Birmingham (GB), University of Genoa (IT), Robert Bosch (Germany) (DE)
UK Research and Innovation, European Commission, HORIZON EUROPE Framework Programme
Partnerships for the goals
Openalex Percentile: Top 23%
Robot Manipulation and Learning
0.98
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