Deep Affordance Foresight: Planning Through What Can Be Done in the Future
Danfei Xu, Ajay Mandlekar, Roberto Martín-Martín, Yuke Zhu, Silvio Savarese, Li Fei-Fei
Abstract
Planning in realistic environments requires searching in large planning spaces. Affordances are a powerful concept to simplify this search, because they model what actions can be successful in a given situation. However, the classical notion of affordance is not suitable for long horizon planning because it only informs the robot about the immediate outcome of actions instead of what actions are best for achieving a long-term goal. In this paper, we introduce a new affordance representation that enables the robot to reason about the longterm effects of actions through modeling what actions are afforded in the future. Based on the new representation, we develop a learning-to-plan method, Deep Affordance Foresight (DAF), that learns partial environment models of affordances of parameterized motor skills through trial-and-error. We evaluate DAF on two challenging manipulation domains and show that it can effectively learn to carry out multi-step tasks, share learned affordance representations among different tasks, and learn to plan with high-dimensional image inputs.
BibTeX
@inproceedings{icra2021_deepaffordancefo,
title = {Deep Affordance Foresight: Planning Through What Can Be Done in the Future},
author = {Danfei Xu and Ajay Mandlekar and Roberto Martín-Martín and Yuke Zhu and Silvio Savarese and Li Fei-Fei},
booktitle = {ICRA 2021},
year = {2021}
}