Allowing robots to communicate naturally with humans is an important goal for social robotics. Most approaches have focused on building high-level probabilistic cognitive models. However, research in cognitive science shows that people often build common ground for communication with each other by seeking and providing evidence of understanding through behaviors like mimicry. Predictive State Representations (PSRs) allow one to build explicit, low-level models of the expected outcomes of actions, and are therefore well-suited for tasks that require providing such evidence of understanding. Using human-robot shadow puppetry as a prototype interaction study, we show that PSRs can be used successfully to both model human interactions, and to allow a robot to learn on-line how to engage a human in an interesting interaction.

Humans rely on a finely tuned ability to recognize and adapt to socially relevant patterns in their everyday face-to-face interactions. This allows them to anticipate the actions of others, coordinate their behaviors, and create shared meaning to communicate. Social robots must likewise be able to recognize and perform relevant social patterns, including interactional synchrony, imitation, and particular sequences of behaviors. We use existing empirical work in the social sciences and observations of human interaction to develop nonverbal interactive capabilities for a robot in the context of shadow puppet play, where people interact through shadows of hands cast against a wall. We show how information theoretic quantities can be used to model interaction between humans and to generate interactive controllers for a robot. Finally, we evaluate the resulting model in an embodied human-robot interaction study. We show the benefit of modeling interaction as a joint process rather than modeling individual agents.

We study the problem of designing motion-planning and sensor assignment strategies for tracking multiple targets with a mobile sensor network. We focus on triangulation based tracking where two sensors merge their measurements in order to estimate the position of a target. We present an iterative and distributed algorithm for the tracking problem. An iteration starts with an initialization phase where targets are assigned to sensor pairs. Afterwards, assigned sensors relocate to improve their estimates. We refer to the problem of computing new locations for sensors (for given target assignments) as one-step tracking. After observing that one-step tracking is computationally hard, we show how it can be formulated as an energy-minimization problem. This allows us to adapt well-studied distributed algorithms for energy minimization. We present simulations to compare the performance of two such algorithms and conclude the paper with a description of the full tracking strategy. The utility of the presented strategy is demonstrated with simulations and experiments on a sensor network platform