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Reactive motion generation problems are usually solved by computing actionsas a sum of policies. However, these policies are independent of each other andthus, they can have conflicting behaviors when summing their contributionstogether. We introduce Composable Energy Policies (CEP), a novel framework formodular reactive motion generation. CEP computes the control action byoptimization over the product of a set of stochastic policies. This product ofpolicies will provide a high probability to those actions that satisfy all thecomponents and low probability to the others. Optimizing over the product ofthe policies avoids the detrimental effect of conflicting behaviors betweenpolicies choosing an action that satisfies all the objectives. Besides, we showthat CEP naturally adapts to the Reinforcement Learning problem allowing us tointegrate, in a hierarchical fashion, any distribution as prior, frommultimodal distributions to non-smooth distributions and learn a new policygiven them.

Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning