We present a framework that allows a robot manipulator to learn how to execute structured tasks from human demonstrations. The proposed system combines physical human-robot interaction with attentional supervision in order to support kinesthetic teaching, incremental learning, and cooperative execution of hierarchically structured tasks. In the proposed framework, the human demonstration is automatically segmented into basic movements, which are related to a task structure by an attentional system that supervises the overall interaction. The attentional system permits to track the human demonstration at different levels of abstraction and supports implicit non-verbal communication both during the teaching and the execution phase. Attention manipulation mechanisms (e.g. object and verbal cueing) can be exploited by the teacher to facilitate the learning process. On the other hand, the attentional system permits flexible and cooperative task execution. The paper describes the overall system architecture and details how cooperative tasks are learned and executed. The proposed approach is evaluated in a human-robot co-working scenario, showing that the robot is effectively able to rapidly learn and flexibly execute structured tasks.

The goal of the paper is to present the foreseen research activity of the European project "SHERPA" whose activities will start officially on February 1th 2013. The goal of SHERPA is to develop a mixed ground and aerial robotic platform to support search and rescue activities in a real-world hostile environment, like the alpine scenario that is specifically targeted in the project. Looking into the technological platform and the alpine rescuing scenario, we plan to address a number of research topics about cognition and control. What makes the project potentially very rich from a scientific viewpoint is the heterogeneity and the capabilities to be owned by the different actors of the SHERPA system: the human rescuer is the "busy genius", working in team with the ground vehicle, as the "intelligent donkey", and with the aerial platforms, i.e. the "trained wasps" and "patrolling hawks". Indeed, the research activity focuses on how the "busy genius" and the "SHERPA animals" interact and collaborate with each other, with their own features and capabilities, toward the achievement of a common goal.

An architecture suitable for the control of multiple unmanned aerial vehicles deployed in Search & Rescue missions is presented in this paper. In the proposed system, a single colocated human operator is able to coordinate the actions of a set of robots in order to retrieve relevant information of the environment. This work is framed in the context of the SHERPA project whose goal is to develop a mixed ground and aerial robotic platform to support search and rescue activities in alpine scenario. Differently from typical human-drone interaction settings, here the operator is not fully dedicated to the drones, but involved in search and rescue tasks, hence only able to provide sparse and incomplete instructions to the robots. In this work, the domain, the interaction framework and the executive system for the autonomous action execution are discussed. The overall system has been tested in a real world mission with two drones equipped with on-board cameras

Safety critical planning and execution is a crucial issue in autonomous systems. This paper proposes a methodology for controller synthesis suitable for timelinebased planning and demonstrates its effectiveness in a space domain where robustness of execution is a crucial property. The proposed approach uses Timed Game Automata (TGA) for formal modeling and the UPPAAL-TIGA model checker for controllers synthesis. An experimental evaluation is performed using a real-world control system.

A robotic system that interacts with humans is expected to flexibly execute structured cooperative tasks while reacting to unexpected events and behaviors. In this paper, we face these issues presenting a framework that integrates cognitive control, executive attention, and hierarchical plan execution. In the proposed approach, the execution of structured tasks is guided by top-down (task-oriented) and bottom-up (stimuli-driven) attentional processes that affect behavior selection and activation, while resolving conflicts and decisional impasses. Specifically, attention is here deployed to stimulate the activations of multiple hierarchical behaviors orienting them toward the execution of finalized and interactive activities. On the other hand, this framework allows a human to indirectly and smoothly influence the robotic task execution exploiting attention manipulation. We provide an overview of the overall system architecture discussing the framework at work in different case studies. In particular, we show that multiple concurrent tasks can be effectively orchestrated and interleaved in a flexible manner; moreover, in a human-robot interaction setting, we test and assess the effectiveness of attention manipulation for interactive plan guidance