# Hierarchical Affordance Discovery using Intrinsic Motivation

arXiv: https://arxiv.org/abs/2009.10968 · HTML: https://mlsanity.com/paper/2009.10968/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy](https://mlsanity.com/paper/2102.09854/) (2021)
- [Rediscovering Affordance: A Reinforcement Learning Perspective](https://mlsanity.com/paper/2112.12886/) (2021)
- [Possibility Before Utility: Learning And Using Hierarchical Affordances](https://mlsanity.com/paper/2203.12686/) (2022)
- [Robots Learn Increasingly Complex Tasks with Intrinsic Motivation and Automatic Curriculum Learning](https://mlsanity.com/paper/2202.10222/) (2022)
- [Self-Supervised Learning of Action Affordances as Interaction Modes](https://mlsanity.com/paper/2305.17565/) (2023)
- [Goal Space Abstraction in Hierarchical Reinforcement Learning via Reachability Analysis](https://mlsanity.com/paper/2309.07168/) (2023)

## Abstract (arXiv)

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.