  Reactive motion generation problems are usually solved by computing actions
as a sum of policies. However, these policies are independent of each other and
thus, they can have conflicting behaviors when summing their contributions
together. We introduce Composable Energy Policies (CEP), a novel framework for
modular reactive motion generation. CEP computes the control action by
optimization over the product of a set of stochastic policies. This product of
policies will provide a high probability to those actions that satisfy all the
components and low probability to the others. Optimizing over the product of
the policies avoids the detrimental effect of conflicting behaviors between
policies choosing an action that satisfies all the objectives. Besides, we show
that CEP naturally adapts to the Reinforcement Learning problem allowing us to
integrate, in a hierarchical fashion, any distribution as prior, from
multimodal distributions to non-smooth distributions and learn a new policy
given them.
