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Titel:

Learning Causal Relationships of Object Properties and Affordances Through Human Demonstrations and Self-Supervised Intervention for Purposeful Action in Transfer Environments

Dokumenttyp:
Magazinartikel
Autor(en):
Constantin Uhde; Nicolas Berberich; Hao Ma; Rogelio Guadarrama; Gordon Cheng
Abstract:
Learning object affordances enables robots to plan and perform purposeful actions. However, a fundamental challenge for the utilization of affordance knowledge lies in its generalization to unknown objects and environments. In this letter we present a new method for learning causal relationships between object properties and object affordances which can be transferred to other environments. Our approach, implemented on a PR2 robot, generates hypotheses of property-affordance models in a toy envi...     »
Zeitschriftentitel:
IEEE Robotics and Automation Letters
Jahr:
2022
Band / Volume:
Volume 7
Jahr / Monat:
2022-10
Heft / Issue:
Issue 4
Seitenangaben Beitrag:
11015 - 11022
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1109/LRA.2022.3196125
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