実世界でのオフライン世界モデルのファインチューニング
Finetuning Offline World Models in the Real World
実機で収集したオフラインデータで世界モデルを事前学習し、モデルベース計画で得たオンラインデータでファインチューニングする手法を提案。計画時に不確実性を考慮することで外挿誤差を抑え、限られたデータでも新規タスクへ少ない試行で適応できることを示した。
著者: Yunhai Feng, Nicklas Hansen, Ziyan Xiong, Chandramouli Rajagopalan, Xiaolong Wang
分類: cs.LG, cs.AI, cs.CV, cs.RO
原文アブストラクト
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 .
