The goals of the research are to develop new robot learning algorithms for qualitatively more complex robots and tasks than have been attempted so far, and to greatly increase the level of automation of implementing robot learning. The research will focus on: 1: tasks and robots with many degrees of freedom, 2: having one robot learn to perform multiple tasks, and generalize appropriately between tasks, 3: learning over a long time span, in which the robot, the task, and the environment change, and 4: combining multiple approaches to robot learning. The research will contribute to a more automatic process for selecting task structure, representations, and adjustable parameters and functions. Two types of learning will be emphasized: learning from demonstration, where the robot learns from a demonstration of how to perform a task, and reinforcement learning, where the robot learns by optimizing a reward function. Flexible methods to represent knowledge will be emphasized, including locally weighted learning and other nonparametric learning techniques. The techniques developed in implementing learning from demonstration will form the basis of the approach to more general learning problems, such as reinforcement learning. This research will be conducted in collaboration with Dr. Mitsuo Kawato at the Advanced Telecommunications Research Human Information Processing Laboratory in Japan. The expected significance of this research is that it will make it easier and less expensive to program robots and machine-based systems in general. From an engineering point of view the goal is to reduce the amount of expensive expert human input into robot programming. From a psychological point of view the goal is to understand how people learn, and this kind of work leads to models of how learning behavior might be accomplished.
