Gaussian Processes for Data-Efficient Learning in Robotics and Control

TL;DR

This paper introduces a data-efficient reinforcement learning method using Gaussian process models to improve learning speed and reduce data requirements in robotics and control tasks.

Contribution

It presents a novel model-based policy search approach that explicitly incorporates Gaussian process uncertainty to enhance learning efficiency in real robotic systems.

Findings

Achieves faster learning compared to state-of-the-art RL methods.

Effectively reduces the number of interactions needed for learning.

Demonstrates applicability on real robot and control tasks.

Abstract

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this article, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.