Recognising Affordances in Predicted Futures to Plan with Consideration of Non-canonical Affordance Effects
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 34 references that are in the map. First posted on arXiv 22 Jun 2022.
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17 of 34 references.
References · 17
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5 more
- 2020Affordances in Robotic Tasks -- A Surveycited by 28
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Citing papers per year: 2026: 1.
1 paper.