Flexible Task Execution and Cognitive Control in Human-Robot Interaction

A robotic system that interacts with humans is expected to flexibly execute structured cooperative tasks while reacting to unexpected events and behaviors. In this thesis, these issues are faced presenting a framework that integrates cognitive control, executive attention, structured task execution and learning. 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 towards 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 applicative contexts. In particular, we show that multiple concurrent tasks/plans 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 and learning processes

Symbolic Task Compression in Structured Task Learning

Learning everyday tasks from human demonstrations requires unsupervised segmentation of seamless demonstrations, which may result in highly fragmented and widely spread symbolic representations. Since the time needed to plan the task depends on the amount of possible behaviors, it is preferable to keep the number of behaviors as low as possible. In this work, we present an approach to simplify the symbolic representation of a learned task which leads to a reduction of the number of possible behaviors. The simplification is achieved by merging sequential behaviors, i.e. behaviors which are logically sequential and act on the same object. Assuming that the task at hand is encoded in a rooted tree, the approach traverses the tree searching for sequential nodes (behaviors) to merge. Using simple rules to assign pre- and post-conditions to each node, our approach significantly reduces the number of nodes, while keeping unaltered the task flexibility and avoiding perceptual aliasing. Experiments on automatically generated and learned tasks show a significant reduction of the planning time

A Flexible Robotic Depalletizing System for Supermarket Logistics

Depalletizing robotic systems are commonly deployed to automatize and speed-up parts of logistic processes. Despite this, the necessity to adapt the preexisting logistic processes to the automatic systems often impairs the application of such robotic solutions to small business realities like supermarkets. In this work we propose a robotic depalletizing system designed to be easily integrated into supermarket logistic processes. The system has to schedule, monitor and adapt the depalletizing process considering both on-line perceptual information given by non-invasive sensors and constraints provided by the high-level management system or by a supervising user. We describe the overall system discussing two case studies in the context of a supermarket logistic process. We show how the proposed system can manage multiple depalletizing strategies and multiple logistic requests

Assistance dogs provide a useful behavioural model to enrich communicative skills of assistance robots

These studies are part of a project aiming to reveal relevant aspects of human–dog interactions, which could serve as a model to design successful human-robot interactions. Presently there are no successfully commercialized assistance robots, however, assistance dogs work efficiently as partners for persons with disabilities. In Study 1, we analyzed the cooperation of 32 assistance dog–owner dyads performing a carrying task. We revealed typical behavior sequences and also differences depending on the dyads' experiences and on whether the owner was a wheelchair user. In Study 2, we investigated dogs' responses to unforeseen difficulties during a retrieving task in two contexts. Dogs displayed specific communicative and displacement behaviors, and a strong commitment to execute the insoluble task. Questionnaire data from Study 3 confirmed that these behaviors could successfully attenuate owners' disappointment. Although owners anticipated the technical competence of future assistance robots to be moderate/high, they could not imagine robots as emotional companions, which negatively affected their acceptance ratings of future robotic assistants. We propose that assistance dogs' cooperative behaviors and problem solving strategies should inspire the development of the relevant functions and social behaviors of assistance robots with limited manual and verbal skills

A Realistic Simulation for Swarm UAVs and Performance Metrics for Operator User Interfaces

Robots have been utilized to support disaster mitigation missions through exploration of areas that are either unreachable or hazardous for human rescuers [1]. The great potential for robotics in disaster mitigation has been recognized by the research community and during the last decade, a lot of research has been focused on developing robotic systems for this purpose. In this thesis, we present a description of the usage and classification of UAVs and performance metrics that affect controlling of UAVs. We also present new contributions to the UAV simulator developed by ECSL and RRL: the integration of flight dynamics of Hummingbird quadcopter, and distance optimization using a Genetic algorithm