Model-based synthesis allows to generate controllers that achieve high-level tasks while satisfying certain properties of interest. However, when controllers are executed on concrete systems, several modeling assumptions may be challenged, jeopardizing their real applicability. This paper presents an integrated system for generating, executing and monitoring optimal-by-construction controllers for multi-robot systems. This system unites the power of Optimization Modulo Theories with the flexibility of an on-line executive, providing optimal controllers for high-level task planning, and runtime feedback on their feasibility. After presenting how our system orchestrates static and runtime components, we demonstrate its capabilities using the RoboCup Logistics League as testbed. We do not only present our final solution but also its chronological development, and draw some general observations for the development of OMT-based approaches.Abstract Model-based synthesis allows to generate controllers that achieve high-level tasks while satisfying certain properties of interest. However, when controllers are executed on concrete systems, several modeling assumptions may be challenged, jeopardizing their real applicability. This paper presents an integrated system for generating, executing and monitoring optimal-byconstruction controllers for multi-robot systems. This system unites the power of Optimization Modulo Theories with the flexibility of an on-line executive, providing optimal controllers for high-level task planning, and runtime feedback on their feasibility. After presenting how our system orchestrates static and runtime components, we demonstrate its capabilities using the RoboCup Logistics League as testbed. We do not only present our final solution but also its chronological development, and draw some general observations for the development of OMT-based approaches.
This report presents the results of a friendly competition for formal verification of continuous and hybrid systems with artificial intelligence (AI) components. Specifically, machine learning (ML) components in cyber-physical systems (CPS), such as feedforward neural networks used as feedback controllers in closed-loop systems are considered, which is a class of systems classically known as intelligent control systems, or in more modern and specific terms, neural network control systems (NNCS). We more broadly refer to this category as AI and NNCS (AINNCS). The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2020. In the second edition of this AINNCS category at ARCH-COMP, four tools have been applied to solve seven different benchmark problems, (in alphabetical order): NNV, OVERT, ReachNN*, and VenMAS. This report is a snapshot of the current landscape of tools and the types of benchmarks for which these tools are suited. Due to the diversity of problems, lack of a shared hardware platform, and the early stage of the competition, we are not ranking tools in terms of performance, yet the presented results probably provide the most complete assessment of current tools for safety verification of NNCS.
In the context of assistive robotics, myocontrol is one of the so-far unsolved problems of upper-limb prosthetics. It consists of swiftly, naturally and reliably converting biosignals, non-invasively gathered from an upper-limb disabled subject, into control commands for an appropriate self-powered prosthetic device. Despite decades of research, traditional surface electromyography cannot yet detect the subject's intent to an acceptable degree of reliability, that is, enforce an action exactly when the subject wants it to be enforced.In this work we tackle one such kind of mismatch between the subject's intent and the response by the myocontrol system, and show that Formal Verification can indeed be used to mitigate it. Eighteen intact subjects were engaged in two Target Achievement Control tests in which a standard myocontrol system was compared with two "repaired" ones, one based on a non-formal technique, and thus enforcing no guarantee of safety, and the other using the Satisfiability Modulo Theories (SMT) technology to rigorously enforce the desired property. The experimental results indicate that both repaired systems exhibit better reliability than the non-repaired one. The SMT-based system causes only a modest increase in the required computational resources with respect to the non-formal technique; as opposed to this, the non-formal technique can be easily implemented in existing myocontrol systems, potentially increasing their reliability.
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