Recognising Affordances in Predicted Futures to Plan With Consideration of Non-Canonical Affordance Effects
Solvi Arnold, Mami Kuroishi, Rin Karashima, Tadashi Adachi, Kimitoshi Yamazaki
Abstract
We propose a novel system for action sequence planning based on a combination of affordance recognition and a neural forward model predicting the effects of affordance execution. By performing affordance recognition on predicted futures, we avoid reliance on explicit affordance effect definitions for multi-step planning. Because the system learns affordance effects from experience data, the system can foresee not just the canonical effects of an affordance, but also situation-specific side-effects. This allows the system to avoid planning failures due to such non-canonical effects, and makes it possible to exploit non-canonical effects for realising a given goal. We evaluate the system in simulation, on a set of test tasks that require consideration of canonical and non-canonical affordance effects.
BibTeX
@inproceedings{ral2023_recognisingaffor,
title = {Recognising Affordances in Predicted Futures to Plan With Consideration of Non-Canonical Affordance Effects},
author = {Solvi Arnold and Mami Kuroishi and Rin Karashima and Tadashi Adachi and Kimitoshi Yamazaki},
booktitle = {RA-L 2023},
year = {2023}
}