Back to Search
Start Over
Recognising Affordances in Predicted Futures to Plan with Consideration of Non-canonical Affordance Effects
- Publication Year :
- 2022
-
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.<br />Comment: 8 pages, 8 figures, video: http://youtu.be/4naJ5IghHcg
- Subjects :
- Computer Science - Robotics
Computer Science - Artificial Intelligence
I.2.9
I.2.6
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2206.10920
- Document Type :
- Working Paper