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Autonomous learning of object manipulation skills can enable robots toacquire rich behavioral repertoires that scale to the variety of objects foundin the real world. However, current motion skill learning methods typicallyrestrict the behavior to a compact, low-dimensional representation, limitingits expressiveness and generality. In this paper, we extend a recentlydeveloped policy search method \cite{la-lnnpg-14} and use it to learn a rangeof dynamic manipulation behaviors with highly general policy representations,without using known models or example demonstrations. Our approach learns a setof trajectories for the desired motion skill by using iteratively refittedtime-varying linear models, and then unifies these trajectories into a singlecontrol policy that can generalize to new situations. To enable this method torun on a real robot, we introduce several improvements that reduce the samplecount and automate parameter selection. We show that our method can acquirefast, fluent behaviors after only minutes of interaction time, and can learnrobust controllers for complex tasks, including putting together a toyairplane, stacking tight-fitting lego blocks, placing wooden rings ontotight-fitting pegs, inserting a shoe tree into a shoe, and screwing bottle capsonto bottles.

Learning Contact-Rich Manipulation Skills with Guided Policy Search