Social development [robots]

Most robots are designed to operate in environments that are either highly constrained (as is the case in an assembly line) or extremely hazardous (such as the surface of Mars). Machine learning has been an effective tool in both of these environments by augmenting the flexibility and reliability of robotic systems, but this is often a very difficult problem because the complexity of learning in the real world introduces very high dimensional state spaces and applies severe penalties for mistakes. Human children are raised in environments that are just as complex (or even more so) than those typically studied in robot learning scenarios. However, the presence of parents and other caregivers radically changes the type of learning that is possible. Consciously and unconsciously, adults tailor their action and the environment to the child. They draw attention to important aspects of a task, help in identifying the cause of errors and generally tailor the task to the child's capabilities. Our research group builds robots that learn in the same type of supportive environment that human children have and develop skills incrementally through their interactions. Our robots interact socially with human adults using the same natural conventions that a human child would use. Our work sits at the intersection of the fields of social robotics (Fong et al., 2003; Breazeal and Scawellan, 2002) and autonomous mental development (Weng et al., 2000). Together, these two fields offer the vision of a machine that can learn incrementally, directly from humans, in the same ways that humans learn from each other. In this article, we introduce some of the challenges, goals, and applications of this research.