Like people, dogs and cats live among technologies that affect their lives. Yet little of this technology has been designed with pets in mind. We are developing systems that interact intelligently with animals to entertain, exercise, and empower them. Currently, we are developing a laser-chasing game, in which dogs or cats are tracked by a ceiling-mounted webcam, and a computer-controlled laser moves with knowledge of the pet's position and movement. Machine learning will be applied to optimize the specific laser strategy. We envision enabling owners to initiate and view the interaction remotely through a web interface, providing stimulation and exercise to pets when the owners are at work or otherwise cannot be present.

Can robots collaboratively exchange stories with children and improve their language and storytelling skills? With our latest Tega robot platform, we aim to develop a deep personalization algorithm based on a long-term interaction with an individual user. Through robot interaction, we collect a corpus of each child's linguistics, narrative, and concept skill information, and develop robot's AI that can generate stories and behaviors personalized to each child's growth level and engagement factors, including affective states.

We are developing a system of early literacy apps, games, toys, and robots that will triage how children are learning, diagnose literacy deficits, and deploy dosages of content to encourage app play using a mentoring algorithm that recommends an appropriate activity given a child's progress. Currently, over 200 Android-based tablets have been sent to children around the world; these devices are instrumented to provide a very detailed picture of how kids are using these technologies. We are using this big data to discover usage and learning models that will inform future educational development.

We are creating a mobile application that adapts traditional expressive writing therapy into the framework of mobile phone technologies. Instead of writing on paper or on a computer device via keyboards, mobile phone users will verbally express themselves and their daily experiences to a virtual agent on their smart phone. The virtual agent will prompt the journaling activity with positive psychology interventions based on the user's affective state, and continuously learn the user's preferences during interactions. We hypothesize that users will gain higher psychological wellbeing through these interactive journaling activities.

To serve us well, robots and other agents must understand our needs and how to fulfill them. To that end, our research develops robots that empower humans by interactively learning from them. Interactive learning methods enable technically unskilled end-users to designate correct behavior and communicate their task knowledge to improve a robot's task performance. This research on interactive learning focuses on algorithms that facilitate teaching by signals of approval and disapproval from a live human trainer. We operationalize these feedback signals as numeric rewards within the machine-learning framework of reinforcement learning. In comparison to the complementary form of teaching by demonstration, this feedback-based teaching may require less task expertise and place less cognitive load on the trainer. Envisioned applications include human-robot collaboration and assistive robotic devices for handicapped users, such as myolectrically controlled prosthetics.

Young children learn language not through listening alone, but through active communication with a social actor. Cultural immersion and context are also key in long-term language development. We are developing robotic conversational partners and hybrid physical/digital environments for language learning. For example, the robot Sophie helped young children learn French through a food-sharing game. The game was situated on a digital tablet embedded in a café table. Sophie modeled how to order food and as the child practiced the new vocabulary, the food was delivered via digital assets onto the table's surface. A teacher or parent can observe and shape the interaction remotely via a digital tablet interface to adjust the robot's conversation and behavior to support the learner. More recently, we have been examining how social nonverbal behaviors impact children's perceptions of the robot as an informant and social companion.

SHARE is a robotic cognitive architecture focused on manipulating and understanding the phenomenon of shared attention during interaction. SHARE incorporates new findings and research in the understanding of nonverbal referential gesture, visual attention system research, and interaction science. SHARE's research incorporates new measurement devices, advanced artificial neural circuits, and a robot that makes its own decisions.

Tega is a new robot platform for long-term interactions with children. The robot leverages smart phones to graphically display facial expressions. Smart phones are also used for computational needs, including behavioral control, sensor processing, and motor control to drive its five degrees of freedom. To withstand long-term continual use, we have designed an efficient battery-powered system that can potentially run for up to six hours before needing to be charged. We also designed for more robust and reliable actuator movements so that the robot can express consistent and expressive behaviors over long periods of time. Through its small size and furry exterior, the robot is aesthetically designed for children. We aim to field test the robot's ability to work reliably in out-of-lab environments and engage young children in educational activities.