Hierarchical Affordance Discovery using Intrinsic Motivation

TL;DR

This paper introduces a novel intrinsic motivation-based algorithm enabling mobile robots to autonomously discover, learn, and adapt interrelated affordances for improved task planning, advancing lifelong autonomous learning.

Contribution

It presents a new algorithm for autonomous affordance discovery in mobile robots using intrinsic motivation, without pre-programmed actions or static setups.

Findings

The algorithm successfully discovers and learns affordances autonomously.

Robots can plan action sequences using learned affordances.

Compared to existing methods, it shows improved adaptability and autonomy.

Abstract

To be capable of lifelong learning in a real-life environment, robots have to tackle multiple challenges. Being able to relate physical properties they may observe in their environment to possible interactions they may have is one of them. This skill, named affordance learning, is strongly related to embodiment and is mastered through each person's development: each individual learns affordances differently through their own interactions with their surroundings. Current methods for affordance learning usually use either fixed actions to learn these affordances or focus on static setups involving a robotic arm to be operated. In this article, we propose an algorithm using intrinsic motivation to guide the learning of affordances for a mobile robot. This algorithm is capable to autonomously discover, learn and adapt interrelated affordances without pre-programmed actions. Once learned, these affordances may be used by the algorithm to plan sequences of actions in order to perform tasks of various difficulties. We then present one experiment and analyse our system before comparing it with other approaches from reinforcement learning and affordance learning.