# Rediscovering Affordance: A Reinforcement Learning Perspective

arXiv: https://arxiv.org/abs/2112.12886 · HTML: https://mlsanity.com/paper/2112.12886/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 83 references that are in the map.
- First posted on arXiv 24 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Spatial Autoencoders for Visuomotor Learning](https://mlsanity.com/paper/1509.06113/) (2015)
- [AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection](https://mlsanity.com/paper/1709.07326/) (2017)
- [Learning to Act Properly: Predicting and Explaining Affordances from Images](https://mlsanity.com/paper/1712.07576/) (2017)
- [Learning Affordance Landscapes for Interaction Exploration in 3D Environments](https://mlsanity.com/paper/2008.09241/) (2020)
- [Meta-learning in natural and artificial intelligence](https://mlsanity.com/paper/2011.13464/) (2020)
- [What can I do here? A Theory of Affordances in Reinforcement Learning](https://mlsanity.com/paper/2006.15085/) (2020)
- [Affordance Learning for End-to-End Visuomotor Robot Control](https://mlsanity.com/paper/1903.04053/) (2019)
- [Adapting User Interfaces with Model-based Reinforcement Learning](https://mlsanity.com/paper/2103.06807/) (2021)
- [Hierarchical Affordance Discovery using Intrinsic Motivation](https://mlsanity.com/paper/2009.10968/) (2020)

## Built on by (most cited first, 4 of 4)

- [ILuvUI: Instruction-tuned LangUage-Vision modeling of UIs from Machine Conversations](https://mlsanity.com/paper/2310.04869/) (2023)
- [Visual-Geometric Collaborative Guidance for Affordance Learning](https://mlsanity.com/paper/2410.11363/) (2024)
- [Cognitive Science-Inspired Evaluation of Core Capabilities for Object Understanding in AI](https://mlsanity.com/paper/2503.21668/) (2025)
- [GhostUI: Unveiling Hidden Interactions in Mobile UI](https://mlsanity.com/paper/2601.19258/) (2026)

## Abstract (arXiv)

Affordance refers to the perception of possible actions allowed by an object. Despite its relevance to human-computer interaction, no existing theory explains the mechanisms that underpin affordance-formation; that is, how affordances are discovered and adapted via interaction. We propose an integrative theory of affordance-formation based on the theory of reinforcement learning in cognitive sciences. The key assumption is that users learn to associate promising motor actions to percepts via experience when reinforcement signals (success/failure) are present. They also learn to categorize actions (e.g., "rotating" a dial), giving them the ability to name and reason about affordance. Upon encountering novel widgets, their ability to generalize these actions determines their ability to perceive affordances. We implement this theory in a virtual robot model, which demonstrates human-like adaptation of affordance in interactive widgets tasks. While its predictions align with trends in human data, humans are able to adapt affordances faster, suggesting the existence of additional mechanisms.