  Meta-reinforcement learning algorithms can enable autonomous agents, such as
robots, to quickly acquire new behaviors by leveraging prior experience in a
set of related training tasks. However, the onerous data requirements of
meta-training compounded with the challenge of learning from sensory inputs
such as images have made meta-RL challenging to apply to real robotic systems.
Latent state models, which learn compact state representations from a sequence
of observations, can accelerate representation learning from visual inputs. In
this paper, we leverage the perspective of meta-learning as task inference to
show that latent state models can \emph{also} perform meta-learning given an
appropriately defined observation space. Building on this insight, we develop
meta-RL with latent dynamics (MELD), an algorithm for meta-RL from images that
performs inference in a latent state model to quickly acquire new skills given
observations and rewards. MELD outperforms prior meta-RL methods on several
simulated image-based robotic control problems, and enables a real WidowX
robotic arm to insert an Ethernet cable into new locations given a sparse task
completion signal after only $8$ hours of real world meta-training. To our
knowledge, MELD is the first meta-RL algorithm trained in a real-world robotic
control setting from images.
