The paper presents a robotic system design methodology based on the concept of an embodied agent decomposed into communicating subsystems, whose activities are specified in terms of FSMs invoking behaviours parameterised by transition functions and terminal conditions. In the implementation phase, this specification is transformed into a system composed of a whiteboard providing communication means and logically labelled FSMs (LLFSMs) defining the system behaviour. These concepts are used to generate the code of the robot controller. The inclusion of inter-subsystem communication model completes the resulting system design with respect to our previous work that did not account for this model. Thus communication plays a central role in this presentation. The design methodology is exemplified with a rudimentary table tennis ball-collecting robot. The presented methodology and the implementation tools are suitable and beneficial for application to the design of other robotic systems.

Interactive machine learning (IML) enables the incorporation of human expertise because the human participates in the construction of the learned model. Moreover, with human-in-the-loop machine learning (HITL-ML), the human experts drive the learning, and they can steer the learning objective not only for accuracy but perhaps for characterisation and discrimination rules, where separating one class from others is the primary objective. Moreover, this interaction enables humans to explore and gain insights into the dataset as well as validate the learned models. Validation requires transparency and interpretable classifiers. The huge relevance of understandable classification has been recently emphasised for many applications under the banner of explainable artificial intelligence (XAI). We use parallel coordinates to deploy an IML system that enables the visualisation of decision tree classifiers but also the generation of interpretable splits beyond parallel axis splits. Moreover, we show that characterisation and discrimination rules are also well communicated using parallel coordinates. In particular, we report results from the largest usability study of a IML system, confirming the merits of our approach.

We propose vectors of finite-state machines whose transitions are labeled by formulas of a common-sense logic as the modeling tool for embedded systems software. We have previously shown that this methodology is very efficient in producing succinct and clear models (e.g., in contrast to plain finite-state machines, Petri nets, or Behavior Trees). We show that we can capture requirements precisely and that we can simulate and validate the models. We can, therefore, directly apply Model-Driven Engineering and deploy the models into software for diverse platforms with full tractability of requirements. Moreover, the sequential semantics of our vector of finite-state machines enables model-checking, formally establishing the correctness of the model. Finally, our approach facilitates systematic Failure Modes and Effects Analysis (FMEA) for diverse target platforms. We demonstrate the effectiveness of our methodology with several examples widely discussed in the software engineering literature and compare this with other approaches, showing that we can prove more properties, and that some claims about verification in such approaches have been exaggerated or are incomplete.

The paper presents a robot control system design method based on hierarchical Finite State Machines (FSMs). The system is decomposed into subsystems. Each one of them is specified in terms of a hierarchical FSM. Previously the description of subsystem activities had been presented as a single, complex FSM, where with each of its states a behaviour was associated. Each behaviour was described by a flowchart. In this proposal behaviours are defined in terms of individual FSMs, resulting in a unified description using only the concept of an FSM. By using FSM specification tools that provide automatic code generation, the robot control system implementation is significantly accelerated. This approach has been verified through the design of a table-tennis ball collecting robot.