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 .

通过使用离线数据集在真实机器人上对世界模型进行预训练，然后通过使用学习模型进行在线数据集的规划和微调，本文试图解决强化学习在真实机器人上训练时的数据效率问题，以及模型在训练和推理过程中的分布偏移问题，该方法在模拟环境和真实机器人上的视觉-动作控制任务上进行了验证，发现即使离线数据有限，该方法也能实现对已知和未知任务的少次数微调。

在真实环境中微调离线世界模型