RI: Small: Robot Developmental Learning of Skilled Actions

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The goal of this project is to show how a robot --- using a continuous stream of visual and tactile data --- can learn to work at a human level of skill in tasks normally done by humans. To function at a human level, it must be able to plan with "object-level" abstractions such as putting a red block into the box, and it must also be able to grasp objects and move them while avoiding bumping into things and causing damage to its surroundings. This project is inspired by human cognitive development. A baby learns about objects and actions by bootstrapping from early regularities and unreliable actions to hierarchies of more complex and reliable actions. The hypothesis to be tested is that this bootstrap learning approach allows a robot to achieve human levels of skillful and robust action in a wide range of human-dominated environments.This project draws on extensive prior work on foundational knowledge representations and machine learning. Learning begins by detecting low-level contingencies --- regularities among observed events --- and refining them into increasingly accurate predictive rules, that can be used to define reliable actions. For a given rule, a simple MDP model is formulated, and reinforcement learning methods learn a policy for accomplishing an action at the next level of the action hierarchy. Learned actions are initially unreliable, but policies and actions improve with experience. Attention is focused where learning is likely to be most productive by intrinsic motivation methods that reward actions that result in successful learning, including the important special case of rewarding attempts to imitate the successful actions of other agents.