  Robot control policies for temporally extended and sequenced tasks are often
characterized by discontinuous switches between different local dynamics. These
change-points are often exploited in hierarchical motion planning to build
approximate models and to facilitate the design of local, region-specific
controllers. However, it becomes combinatorially challenging to implement such
a pipeline for complex temporally extended tasks, especially when the
sub-controllers work on different information streams, time scales and action
spaces. In this paper, we introduce a method that can compose diverse policies
comprising motion planning trajectories, dynamic motion primitives and neural
network controllers. We introduce a global goal scoring estimator that uses
local, per-motion primitive dynamics models and corresponding activation
state-space sets to sequence diverse policies in a locally optimal fashion. We
use expert demonstrations to convert what is typically viewed as a
gradient-based learning process into a planning process without explicitly
specifying pre- and post-conditions. We first illustrate the proposed framework
using an MDP benchmark to showcase robustness to action and model dynamics
mismatch, and then with a particularly complex physical gear assembly task,
solved on a PR2 robot. We show that the proposed approach successfully
discovers the optimal sequence of controllers and solves both tasks
efficiently.
