  Reinforcement learning methods can achieve significant performance but
require a large amount of training data collected on the same robotic platform.
A policy trained with expensive data is rendered useless after making even a
minor change to the robot hardware. In this paper, we address the challenging
problem of adapting a policy, trained to perform a task, to a novel robotic
hardware platform given only few demonstrations of robot motion trajectories on
the target robot. We formulate it as a few-shot meta-learning problem where the
goal is to find a meta-model that captures the common structure shared across
different robotic platforms such that data-efficient adaptation can be
performed. We achieve such adaptation by introducing a learning framework
consisting of a probabilistic gradient-based meta-learning algorithm that
models the uncertainty arising from the few-shot setting with a low-dimensional
latent variable. We experimentally evaluate our framework on a simulated
reaching and a real-robot picking task using 400 simulated robots generated by
varying the physical parameters of an existing set of robotic platforms. Our
results show that the proposed method can successfully adapt a trained policy
to different robotic platforms with novel physical parameters and the
superiority of our meta-learning algorithm compared to state-of-the-art methods
for the introduced few-shot policy adaptation problem.
