Hierarchical Affordance Discovery using Intrinsic Motivation
This work addresses the challenge of lifelong affordance learning for mobile robots, which is incremental as it builds on prior methods by extending them to dynamic, non-static setups.
The paper tackles the problem of enabling mobile robots to autonomously learn and adapt affordances (relationships between environmental properties and possible interactions) without pre-programmed actions, using intrinsic motivation, and demonstrates its capability to plan action sequences for tasks of varying difficulties through an experimental comparison with existing methods.
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.