Recognising Affordances in Predicted Futures to Plan With Consideration of Non-Canonical Affordance Effects
نویسندگان
چکیده
We propose a novel system for action sequence planning based on combination of affordance recognition and neural forward model predicting the effects execution. By performing predicted futures, we avoid reliance explicit effect definitions multi-step planning. Because learns from experience data, can foresee not just canonical an affordance, but also situation-specific side-effects. This allows to failures due such non-canonical effects, makes it possible exploit realising given goal. evaluate in simulation, set test tasks that require consideration effects.
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ژورنال
عنوان ژورنال: IEEE robotics and automation letters
سال: 2023
ISSN: ['2377-3766']
DOI: https://doi.org/10.1109/lra.2023.3239308