Statistical Model Checking of Human-Robot Interaction Scenarios

Robots are soon going to be deployed in non-industrial environments. Before society can take such a step, it is necessary to endow complex robotic systems with mechanisms that make them reliable enough to operate in situations where the human factor is predominant. This calls for the development of robotic frameworks that can soundly guarantee that a collection of properties are verified at all times during operation. While developing a mission plan, robots should take into account factors such as human physiology. In this paper, we present an example of how a robotic application that involves human interaction can be modeled through hybrid automata, and analyzed by using statistical model-checking. We exploit statistical techniques to determine the probability with which some properties are verified, thus easing the state-space explosion problem. The analysis is performed using the Uppaal tool. In addition, we used Uppaal to run simulations that allowed us to show non-trivial time dynamics that describe the behavior of the real system, including human-related variables. Overall, this process allows developers to gain useful insights into their application and to make decisions about how to improve it to balance efficiency and user satisfaction.Comment: In Proceedings AREA 2020, arXiv:2007.1126

Teaching Formal Methods to Software Engineers through Collaborative Learning (Short Paper)

It is common knowledge among researchers in the field that teaching formal methods can prove a challenging task. This paper reports on the approach adopted for a Master’s Degree course at Politecnico di Milano, Italy, as an attempt to reverse this trend by introducing collaborative learning activities. Students put concepts learned during theoretical lectures into practice through a hands-on group assignment. Each group develops the formal model of a Cyber-Physical System through the Uppaal tool, starting from a set of requirements provided by the instructor. After delivering the assignment, we invite students to fill an evaluation survey whose results suggest a very high satisfaction level towards the hybrid theoretical-practical approach of the course

Model-Driven Development of Formally Verified Human-Robot Interactions

Service robots will operate in unconstrained environments due to the significant presence of humans. We present a model-driven framework based on formal methods to develop interactive robotic applications designed to handle the uncertainty of human behavior. Users formally model the human-robot interaction scenario, estimate the most likely outcome, and subsequently deploy the application. Collected traces constitute the data pool for an active automata learning algorithm to update the human model based on the accumulated knowledge. We validate the framework on realistic use cases from the healthcare setting

A Deployment Framework for Formally Verified Human-Robot Interactions

In the future, assistive robots will spread to everyday settings and regularly interact with humans. This paper introduces a deployment approach for assistive robotic applications where human-robot interaction is the main element. The deployment infrastructure hinges on a model-to-code transformation technique and a ROS-based middleware layer and enables deployment in real life or simulation in a virtual environment. The approach fits into a model-driven framework for the formal verification of interactive scenarios. At design-time, the application analyst estimates the most likely outcome of the robotic mission through Statistical Model Checking of a Stochastic Hybrid Automata network modeling the scenario. We introduce an innovative approach to convert a specific subset of Stochastic Hybrid Automata into executable code to control the robot and respond to human actions. Deploying or simulating the application allows analysts to validate the results obtained at design time or to refine the formal model based on runs in the real or the virtual scene. The methodology’s effectiveness is tested via simulation of use cases from the healthcare setting, which can significantly benefit from this kind of approach thanks to its innovative features related to human physiology and autonomous behavior

Formally-based Model-Driven Development of Collaborative Robotic Applications

The development of Human Robot Collaborative (HRC) systems faces many challenges. First, HRC systems should be adaptable and re-configurable to support fast production changes. However, in the development of HRC applications safety considerations are of paramount importance, as much as classical activities such as task programming and deployment. Hence, the reconfiguration and reprogramming of executing tasks might be necessary also to fulfill the desired safety requirements. Model-based software engineering is a suitable means for agile task programming and reconfiguration. We propose a model-based design-to-deployment toolchain that simplifies the routine of updating or modifying tasks. This toolchain relies on (i) UML profiles for quick model design, (ii) formal verification for exhaustive search for unsafe situations (caused by intended or unintended human behavior) within the model, and (iii) trans-coding tools for automating the development process. The toolchain has been evaluated on a few realistic case studies. In this paper, we show a couple of them to illustrate the applicability of the approach