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We propose a novel system for action sequence planning based on a combinationof affordance recognition and a neural forward model predicting the effects ofaffordance execution. By performing affordance recognition on predictedfutures, we avoid reliance on explicit affordance effect definitions formulti-step planning. Because the system learns affordance effects fromexperience data, the system can foresee not just the canonical effects of anaffordance, but also situation-specific side-effects. This allows the system toavoid planning failures due to such non-canonical effects, and makes itpossible to exploit non-canonical effects for realising a given goal. Weevaluate the system in simulation, on a set of test tasks that requireconsideration of canonical and non-canonical affordance effects.

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