Mauro Dragone

Robotic UBIquitous COgnitive Network

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them self-adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The EU FP7 project RUBICON develops self-sustaining learning solutions yielding cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, agent control systems, wireless sensor networks and machine learning. This paper briefly illustrates how these techniques are being extended, integrated, and applied to AAL applications.

SoSAA: a framework for integrating components & agents

Modern computing systems require powerful software frameworks to ease their development and manage their complexity. These issues are addressed within both Component-Based Software Engineering and Agent-Oriented Software Engineering, although few integrated solutions exist. This paper discusses a novel integration strategy, which builds upon both paradigms to address their shortcomings while leveraging their different characteristics to define a complete software framework.

Robotic Ubiquitous Cognitive Ecology for Smart Homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent-based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a proof of concept smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.

Using Mixed Reality Agents as Social Interfaces for Robots

Endowing robots with a social interface is often costly and difficult. Virtual characters on the other hand are comparatively cheap and well equipped but suffer from other difficulties, most notably their inability to interact with the physical world. This paper details our wearable solution to combining physical robots and virtual characters into a mixed reality agent (MiRA) through mixed reality visualisation. It describes a pilot study demonstrating our system, and showing how such a technique can offer a viable alternative cost effective approach to enabling a rich social interface for human-robot interaction.

Mixing robotic realities

This paper contests that Mixed Reality (MR) offers a potential solution in achieving transferability between Human Computer Interaction (HCI) and Human Robot Interaction (HRI). Virtual characters (possibly of a robotic genre) can offer highly expressive interfaces that are as convincing as a human, are comparably cheap and can be easily adapted and personalized. We introduce the notion of a mixed reality agent, i.e. an agent consisting of a physical robotic body and a virtual avatar displayed upon it. We realized an augmented reality interface with a Head-Mounted Display (HMD) in order to interact with such systems and conducted a pilot study to demonstrate the usefulness of mixed reality agents in human-robot collaborative tasks.

Social interaction between robots, avatars & humans

With the recent development of the field of social robotics and in particular the need to negotiate explicit social interaction and behaviour between both socially capable robots and between robots and humans, this work presents the development of a framework which supports coherent social interaction between real and artificial systems. The social robot architecture is implemented in conjunction with the virtual robotic workbench, a cohesive framework which integrates physical robots, virtual robot avatars and humans in a shared social space.

A Programming Framework for Multi-agent Coordination of Robotic Ecologies

Building smart environments with Robotic ecologies, comprising of distributed sensors, actuators and mobile robot devices facilitates and extends the nature and form of smart environments that can be developed, and reduces the complexity and cost of such solutions. While the potentials of such an approach makes robotic ecologies increasingly popular, many fundamental research questions remain open. One such question is how to make a robotic ecology self-adaptive, so as to adapt to changing conditions and evolving requirements, and consequently reduce the amount of preparation and pre-programming required for their deployment in real world applications. This paper presents a framework for the specification and the programming of robotic ecologies. The framework extends an existing agent system and integrates it with the pre-existing and dominant traditional robotic and middleware approach to the development of robotic ecologies. We illustrate how these technologies complement each other and offer a candidate technology to pursue adaptive robotic ecologies.

A cognitive robotic ecology approach to self-configuring and evolving AAL systems

Robotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new users activities and changing users habits.

Real-time adaptive child-robot interaction: Age and gender determination of children based on 3D body metrics

Service robots employed in public spaces need to be equipped with specific sensing, reasoning and human-robot interaction capabilities to adapt their interaction style and thus effectively engage with a variety of users. In this paper we present a method used by an ubiquitous robotic system to gather 3D body metrics and use them to robustly estimate age and gender of previously unseen participants in real-world multi-party situations. We evaluate systems performance on 428 children volunteers and compare them with those obtainable with a state of the art software based on face analysis. This work demonstrates that even small number of biometrics can achieve good age and gender estimation results in perceptually challenging environments. Finally, this paper illustrates how the system is used to inform the online adaptation of the behavior of a humanoid robot.

Investigating the impact of gender development in child-robot interaction

In order to inform the design of robotic applications for children, in this paper we describe and report the results of an experiment we conducted in a primary school. Our work investigates the effects of the robots perceived gender and age on levels of engagement and acceptance of the robot by children across different age and gender groups. Our results show that children across ages relate differently toward perceived robots age and gender.