From Continual Learning to Causal Discovery in Robotics

TL;DR

This paper discusses how continual learning can address practical challenges in causal discovery for autonomous robots, enabling more accurate models with limited computational resources by leveraging the robot's active role.

Contribution

It proposes a novel approach to integrate continual learning into causal discovery processes specifically tailored for robotics applications.

Findings

Continual learning can improve causal model accuracy in robotics.

Robots can actively enhance causal discovery through interaction.

The approach is feasible with limited computational resources.

Abstract

Reconstructing accurate causal models of dynamic systems from time-series of sensor data is a key problem in many real-world scenarios. In this paper, we present an overview based on our experience about practical challenges that the causal analysis encounters when applied to autonomous robots and how Continual Learning~(CL) could help to overcome them. We propose a possible way to leverage the CL paradigm to make causal discovery feasible for robotics applications where the computational resources are limited, while at the same time exploiting the robot as an active agent that helps to increase the quality of the reconstructed causal models.