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.

We address the problem of estimating a person's body height from a single uncalibrated image. The novelty of our work lies in that we handle two difficult cases not previously addressed in the literature: (i) the image contains no reference length in the background scene to indicate absolute scale, (ii) the image contains the upper body part only. In a nutshell, our method combines well-known ideas from projective geometry and single-view metrology with prior probabilistic/statistical knowledge of human anthropometry, in a Bayesian-like framework. The method is demonstrated with synthetic (randomly generated) data as well as a dataset of 96 frontal images.