Like people, dogs and cats live among technologies that affects their lives. Yet little of this technology has been designed with these 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 is moved 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.

After meeting someone for the first time, we come away with an intuitive sense of how much we can trust that person. Nonverbal behaviors, such as gaze patterns, body language, and facial expressions, have been explored as honest or “leaky” signals that are salient cues in gaining trust insights. Our research is toward creating a computational model for recognizing interpersonal trust in social interactions. By observing the trust-related nonverbal cues expressed in the social interaction, we aim to design a machine learning algorithm that is capable of differentiating whether a person finds their socially assistive robot to be a trustworthy or untrustworthy partner. We aim to enable robots to understand our nonverbal signals. With so much of our communication being passed in these nonverbal streams, we hope that by enabling robots to understand these signals, we can design robots that can more effectively communicate with us.

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.

Our mission is to develop the computational techniques that will enable the design, implementation, and evaluation of "relational" robots, in order to encourage the social, emotional, and cognitive growth in children, including those with social or cognitive deficits. Funding for the project comes from the NSF Expeditions in Computing program. This Expedition has the potential to substantially impact the effectiveness of education and healthcare, and to enhance the lives of children and other groups that require specialized support and intervention. In particular, the MIT effort is focusing on developing second language learning companions for pre-school aged children, ultimately for ESL (English as a Second Language).

Two robot arms are in constant motion and hard at work. From a distance, they can be seen considering their tasks, communicating with each other, and struggling to make sense of their abstract mission. Participants are encouraged to approach the hands to engage in a dialog with the robots to offer assistance or advice. The project demonstrates a chatter system to simulate affective conversation and a parametric animation engine to provide dynamic, autonomous character-driven movement in the robots. This project premiered at The Other Festival 2013.

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 reading. 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 technology. We are using this big data to discover usage and learning models that will inform future educational development.