The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning

TL;DR

This paper reviews the significance of uncertainty in embodied AI and robot learning, emphasizing the need to quantify and evaluate uncertainty for deploying deep neural network-based robots in real-world scenarios.

Contribution

It provides an overview of uncertainty's role in robotics and discusses methods to quantify and evaluate it in practical applications.

Findings

Uncertainty is crucial for safe and reliable robot operation.

Methods to quantify uncertainty are essential for real-world deployment.

Understanding diversity in predictions improves robot decision-making.

Abstract

Uncertainty has long been a critical area of study in robotics, particularly when robots are equipped with analytical models. As we move towards the widespread use of deep neural networks in robots, which have demonstrated remarkable performance in research settings, understanding the nuances of uncertainty becomes crucial for their real-world deployment. This guide offers an overview of the importance of uncertainty and provides methods to quantify and evaluate it from an applications perspective.