  We tackle the problem of generalization to unseen configurations for dynamic
tasks in the real world while learning from high-dimensional image input. The
family of nonlinear dynamical system-based methods have successfully
demonstrated dynamic robot behaviors but have difficulty in generalizing to
unseen configurations as well as learning from image inputs. Recent works
approach this issue by using deep network policies and reparameterize actions
to embed the structure of dynamical systems but still struggle in domains with
diverse configurations of image goals, and hence, find it difficult to
generalize. In this paper, we address this dichotomy by leveraging embedding
the structure of dynamical systems in a hierarchical deep policy learning
framework, called Hierarchical Neural Dynamical Policies (H-NDPs). Instead of
fitting deep dynamical systems to diverse data directly, H-NDPs form a
curriculum by learning local dynamical system-based policies on small regions
in state-space and then distill them into a global dynamical system-based
policy that operates only from high-dimensional images. H-NDPs additionally
provide smooth trajectories, a strong safety benefit in the real world. We
perform extensive experiments on dynamic tasks both in the real world (digit
writing, scooping, and pouring) and simulation (catching, throwing, picking).
We show that H-NDPs are easily integrated with both imitation as well as
reinforcement learning setups and achieve state-of-the-art results. Video
results are at https://shikharbahl.github.io/hierarchical-ndps/
