  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.
