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Recognising Affordances in Predicted Futures to Plan with Consideration of Non-canonical Affordance Effects

2022-06-22 09:01:01
Solvi Arnold, Mami Kuroishi, Tadashi Adachi, Kimitoshi Yamazaki
arXiv_AI
       
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.

Abstract (translated)

URL

https://arxiv.org/abs/2206.10920

PDF

https://arxiv.org/pdf/2206.10920.pdf

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