Finetuning Offline World Models in the Real World
(2310.16029)Abstract
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 .
Overview
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Proposes a framework for fine-tuning pre-trained world models on real robots with limited online data through test-time regularization.
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Utilizes Model-based Reinforcement Learning (MBRL) for its data efficiency but addresses challenges in real-world application.
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Introduces a novel approach that balances estimated returns and model uncertainty, leveraging an ensemble of Q-functions for cautious exploration.
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Demonstrates significant advantages over existing RL methods in both simulated environments and real-world robotic tasks with minimal online data.
Fine-tuning Offline World Models for Real-World Visuo-Motor Control Tasks
Introduction
Model-based Reinforcement Learning (MBRL) has proven its prowess in data-efficient learning, essentially by modeling the dynamics of the environment or the so-called "world models". Despite this advantage, applying MBRL directly in real-world scenarios, particularly on real robots, poses significant challenges due to the necessity for immense data collection, which is often impractical or too expensive. Unlike conventional methods that either rely on extensive online interaction or pre-existing datasets with their respective drawbacks, this paper innovatively proposes a framework that begins with pretraining a world model on offline data collected on a real robot, followed by fine-tuning this model with limited online data through carefully designed test-time regularization to balance estimated returns and model uncertainty.
Preliminaries: The Role of MBRL and Reinforcement Learning
The paper targets the inefficiencies in general Reinforcement Learning (RL) strategies where large volumes of data are required for learning skills, specifically in visuo-motor control tasks executed by physical robots. It leverages MBRL for its data efficiency but acknowledges challenges in applying traditional MBRL techniques directly due to extrapolation errors that arise when planning with learned models on unseen state-action pairs. The underlying MBRL framework utilized is TD-MPC, known for its efficient learning through planning and the use of a predictive model.
Approach: Fine-tuning with Regularized Planning
The crux of the proposed method lies in the fine-tuning stage where a novel test-time regularization based on model uncertainty is introduced to mitigate extrapolation errors during planning. Apart from just leveraging offline data, the methodology encompasses the collection of new data in real-time, ensuring that planning decisions are influenced by both past experiences and newly observed environmental interactions. Notably, an ensemble of $Q$-functions calculates the uncertainty, enabling cautious exploration during planning and fine-tuning phases. The interplay between estimated returns and epistemic model uncertainty forms the cornerstone of balanced decision making in unseen tasks or task variations with minimal online data.
Results: Validation on Real and Simulated Visuo-Motor Tasks
The efficacy of the proposed method is demonstrated through a series of experiments on a variety of continuous control tasks spanning both simulated environments and real-world robotic setups. The method showcased a significant advantage over state-of-the-art offline and online RL methods, achieving higher success rates in offline-to-online task transfers. Particularly in real-world scenarios, the method achieved remarkable few-shot fine-tuning performance, adjusting to new task variations effectively within an extremely limited number of trials.
Discussion and Future Directions
While the proposed framework paves the way for efficient RL application in real-world robotic tasks with limited online data, several areas for future exploration remain open. These include addressing the varying impacts of offline data quantity and quality, optimizing the hyperparameters related to uncertainty regularization, and exploring the framework's adaptability to more diverse tasks with possibly more complex dynamics.
Conclusion
This work presents a groundbreaking step towards bridging the gap between offline data-driven learning and real-world robot application through an MBRL framework enhanced with innovative fine-tuning strategies. By effectively managing the intrinsic challenge of extrapolation errors through test-time regularization, and strategically utilizing offline and online data, the proposed method sets a new benchmark for data-efficient reinforcement learning in robotics.
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