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Learning Multi-Object Symbols for Manipulation with Attentive Deep Effect Predictors
[article]
2022
arXiv
pre-print
In this paper, we propose a concept learning architecture that enables a robot to build symbols through self-exploration by interacting with a varying number of objects. Our aim is to allow a robot to learn concepts without constraints, such as a fixed number of interacted objects or pre-defined symbolic structures. As such, the sought architecture should be able to build symbols for objects such as single objects that can be grasped, object stacks that cannot be grasped together, or other
arXiv:2208.01021v1
fatcat:hxu2nn6kqfbyfffzgq6qqxmkym