Skill-Based Architectures in Autonomous systems: Lessons Learnt
Résumé
The deployment of complex autonomous systems into open environment calls for robust, modular and verifiable software architectures. Skillset models, which encapsulate the capabilities of the autonomous robots into modular Skills, have emerged to address these challenges. Expressed within an intermediary layer of the robot's architecture, Skillsets break down high level actions produced by the Deliberative layer into lower level actions, executed by functional components. The use of a Domain Specific Language (DSL) for Skillset modeling allow formal methods to provide robust code generation and model verification. However, as Skill-based architectures are more and more developed, the true benefits of opting for such an approach have yet to be investigated. Marking the beginning of a PhD thesis, this extended abstract presents an early-stage analysis of recent Skill-based architectures making use of the ONERA Robot Skill Toolchain with a focus on identifying best practices, common pitfalls, and offering guidelines for future developments. The experiment relies on the works of several french laboratories all making use of the Robot Language DSL formalized by Albore et al. The findings suggest that while Skill-based architectures present undeniable advantages in the development of autonomous systems, there is currently a need for more standardized frameworks.
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