Although existing robotic systems are interesting to interact with in the short term, it has been shown that after some weeks of quasi-regular encounters, humans gradually lose their interest, and meaningful longer-term human-robot relationships are not established. An underlying hypothesis driving the proposed project is that such relationships can be significantly enhanced if the human and the robot are gradually creating a pool of shared episodic memories that they can co-refer to, and if they are both embedded in a social web of other humans and robots they both know and encounter frequently. Thus, here we propose to use Facebook, a highly successful online networking resource for humans, towards enhancing longer-term human-robot relationships, by helping to address the above two prerequisites. As a starting point, we utilize social information in order to personalize human-robot dialogues, and to include references to past encounters and to encounters with friends within dialogues. A robot equipped with a modular software architecture (with IPC-intercommunicating modules for face recognition, a simple dialog system, a navigation subsystem, and a real-time Facebook connection/local social database) has been deployed, and is encountering humans in the environment of our lab. An early demonstration of a basic form of such encounters is shown in the submitted video. The system is expected to achieve two significant novelties: arguably being one of the first robots to be embedded in a social web, and being the first robot that can purposefully exploit and create social information that is available online. Furthermore, it is expected to provide empirical support for our main driving hypothesis, that the formation of shared episodic memories within a social web can lead to more meaningful long-term human-robot relationships.

Our project aims at supporting the creation of sustainable and meaningful longer-term human-robot relationships through the creation of embodied robots with face recognition and natural language dialogue capabilities, which exploit and publish social information available on the web (Facebook). Our main underlying experimental hypothesis is that such relationships can be significantly enhanced if the human and the robot are gradually creating a pool of shared episodic memories that they can co-refer to (shared memories), and if they are both embedded in a social web of other humans and robots they both know and encounter (shared friends). In this paper, we are presenting such a robot, which as we will see achieves two significant novelties.

Self-directed learning is an essential component of artificial and biological intelligent systems that are required to interact with and adapt to complex real world environments. Inspired by psychological and neuroscientific data, many algorithms and architectures have been proposed in the field of developmental robotics that use novelty as a training signal. Such approaches are aimed at motivating the exploration of sensory-motor contingencies for which mental models have not yet been accurately formed, driving the agent to develop task-independent competencies (such as understanding object affordances) without the need for explicit teaching. However, novelty-driven exploration on its own leads to a number of well-known problems that impede competence acquisition such as the attraction of agents to chaotic or unlearnable tasks and the temporary oversampling of aspects of the environment until they are no longer novel. This paper contributes to the field, taking insight from neuroscientific data on selective attention (particularly the temporary "boredom" associated with recently seen stimuli and a counter preference for the familiar), to propose mechanisms that may help address the noted problems relating to developmental learning in robots. Experiments conducted on an AIBO ERS-7 robotic dog demonstrate the potential of the approach