  Reinforcement Learning (RL) is notoriously data-inefficient, which makes
training on a real robot difficult. While model-based RL algorithms (world
models) improve data-efficiency to some extent, they still require hours or
days of interaction to learn skills. Recently, offline RL has been proposed as
a framework for training RL policies on pre-existing datasets without any
online interaction. However, constraining an algorithm to a fixed dataset
induces a state-action distribution shift between training and inference, and
limits its applicability to new tasks. In this work, we seek to get the best of
both worlds: we consider the problem of pretraining a world model with offline
data collected on a real robot, and then finetuning the model on online data
collected by planning with the learned model. To mitigate extrapolation errors
during online interaction, we propose to regularize the planner at test-time by
balancing estimated returns and (epistemic) model uncertainty. We evaluate our
method on a variety of visuo-motor control tasks in simulation and on a real
robot, and find that our method enables few-shot finetuning to seen and unseen
tasks even when offline data is limited. Videos, code, and data are available
at https://yunhaifeng.com/FOWM .
