Bootstrapping vehicles: A formal approach to unsupervised sensorimotor learning based on invariance.

摘要

In principle, could a "brain in a jar" be able to control an unknown robotic body to which it is connected, and use it to achieve useful tasks, without any previous assumptions on the robotic body's sensors and actuators? Other than as an interesting theoretical issue, this question is relevant to the medium-term challenges of robotics: as the complexity of robotics applications grows, automated learning techniques might reduce design effort and increase the robustness and reliability of the solutions. In this work, the problem of "bootstrapping" is studied in the context of the Vehicles universe, which is an idealization of simple mobile robots, after the work of Braitenberg. The first thread of results consists in analyzing such simple sensorimotor cascades and proposing models of varying complexity that can be learned from data. The second thread regards how to properly formalize the notions of "absence of assumptions", as a particular form of invariance that the bootstrapping agent must satisfy, and proposes some invariance-based design techniques.