  Learning algorithms are enabling robots to solve increasingly challenging
real-world tasks. These approaches often rely on demonstrations and reproduce
the behavior shown. Unexpected changes in the environment may require using
different behaviors to achieve the same effect, for instance to reach and grasp
an object in changing clutter. An emerging paradigm addressing this robustness
issue is to learn a diverse set of successful behaviors for a given task, from
which a robot can select the most suitable policy when faced with a new
environment. In this paper, we explore a novel realization of this vision by
learning a generative model over policies. Rather than learning a single
policy, or a small fixed repertoire, our generative model for policies
compactly encodes an unbounded number of policies and allows novel controller
variants to be sampled. Leveraging our generative policy network, a robot can
sample novel behaviors until it finds one that works for a new environment. We
demonstrate this idea with an application of robust ball-throwing in the
presence of obstacles. We show that this approach achieves a greater diversity
of behaviors than an existing evolutionary approach, while maintaining good
efficacy of sampled behaviors, allowing a Baxter robot to hit targets more
often when ball throwing in the presence of obstacles.
