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In order for robots to collaborate with humans, they need to be able to accurately forecast human intent and action. People act with purpose: that is, they make sequences of decisions to achieve long-term objectives. For instance, in driving from home to a store, people carefully plan a sequence of roads that will get them there efficiently. In predicting a person's next decision, algorithms must be developed that reflect these purposeful actions.
Currently, robots are unable to anticipate human needs and goals, and this represents a fundamental barrier to their large-scale deployment in the home and workplace. The aim of this project is to develop a new science of purposeful prediction that can be applied to human-robot interaction across a wide variety of domains. The work draws on recent techniques based on Inverse Optimal Control and Inverse Equilibria Theory that enable statistically sound reasoning about observed deliberate behavior. These new methods provide the foundations of a theoretical framework that integrates traditional decision making techniques like optimal control, search and planning with probabilistic methods that reason about uncertainty and hidden information, particularly about goals, utility and intent.
Intellectual merit: The project will provide a general framework that allows robots to anticipate and adapt to the activities of their human co-workers based on perceptual cues. The investigators will develop the theory, a computational toolbox, and, in collaboration with industrial partners, prototype deployments of these new methods for the prediction of peoples' behavior in a diverse set of robotics domains from computer vision to motor control. The project is transformative in that it combines a novel theoretical/algorithmic framework with extensive support in terms of volume of data and validation infrastructure in the context of many applications.
Broader impacts: A revolution in personal robotics in both the home and workplace depends on the ability to forecast human activities and intents; small- and medium- scale manufacturing will make a leap forward through agile robotic systems intelligent enough to understand and assist their co-workers in flexible assembly tasks; and robust models of pedestrian and vehicular traffic flow will enable more effective driver warning systems and safer autonomous mobile robots. Purposeful prediction technology is an important step towards enabling such understanding of actions and intents in these arenas. The research work will involve the training and mentoring of undergraduate, masters and doctoral students as well as post-doctoral fellows in this emerging multi-disciplinary research area at the intersection of computer and cognitive sciences and robotics.