# Affordance as general value function: A computational model

arXiv: https://arxiv.org/abs/2010.14289 · HTML: https://mlsanity.com/paper/2010.14289/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning](https://mlsanity.com/paper/2111.03189/) (2021)
- [From Screens to Scenes: A Survey of Embodied AI in Healthcare](https://mlsanity.com/paper/2501.07468/) (2025)
- [Toward Enactive Artificial Intelligence](https://mlsanity.com/paper/2605.24238/) (2026)
- [Affordance Perception by a Knowledge-Guided Vision-Language Model with Efficient Error Correction](https://mlsanity.com/paper/2407.13368/) (2024)
- [Synthetic Linguistic Agency: How an Embodied Mortal Agent Learns Linguistic Affordances through Consequential Social Experience](https://mlsanity.com/paper/2608.27843/) (2026)

## Abstract (arXiv)

General value functions (GVFs) in the reinforcement learning (RL) literature are long-term predictive summaries of the outcomes of agents following specific policies in the environment. Affordances as perceived action possibilities with specific valence may be cast into predicted policy-relative goodness and modelled as GVFs. A systematic explication of this connection shows that GVFs and especially their deep learning embodiments (1) realize affordance prediction as a form of direct perception, (2) illuminate the fundamental connection between action and perception in affordance, and (3) offer a scalable way to learn affordances using RL methods. Through an extensive review of existing literature on GVF applications and representative affordance research in robotics, we demonstrate that GVFs provide the right framework for learning affordances in real-world applications. In addition, we highlight a few new avenues of research opened up by the perspective of "affordance as GVF", including using GVFs for orchestrating complex behaviors.