  Dexterous multi-fingered hands can accomplish fine manipulation behaviors
that are infeasible with simple robotic grippers. However, sophisticated
multi-fingered hands are often expensive and fragile. Low-cost soft hands offer
an appealing alternative to more conventional devices, but present considerable
challenges in sensing and actuation, making them difficult to apply to more
complex manipulation tasks. In this paper, we describe an approach to learning
from demonstration that can be used to train soft robotic hands to perform
dexterous manipulation tasks. Our method uses object-centric demonstrations,
where a human demonstrates the desired motion of manipulated objects with their
own hands, and the robot autonomously learns to imitate these demonstrations
using reinforcement learning. We propose a novel algorithm that allows us to
blend and select a subset of the most feasible demonstrations to learn to
imitate on the hardware, which we use with an extension of the guided policy
search framework to use multiple demonstrations to learn generalizable neural
network policies. We demonstrate our approach on the RBO Hand 2, with learned
motor skills for turning a valve, manipulating an abacus, and grasping.
