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• W4319990410 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." @default.
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• W4319990410 date "2023-03-01" @default.
• W4319990410 modified "2023-09-26" @default.
• W4319990410 title "Recognising Affordances in Predicted Futures to Plan With Consideration of Non-Canonical Affordance Effects" @default.
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• W4319990410 doi "https://doi.org/10.1109/lra.2023.3239308" @default.
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