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Exploration with intrinsic motivation using object–action–outcome latent space
(IEEE, 2023-06)
One effective approach for equipping artificial agents with sensorimotor skills is to use self-exploration. To do this efficiently is critical, as time and data collection are costly. In this study, we propose an exploration ...
Deep multi-object symbol learning with self-attention based predictors
(IEEE, 2023)
This paper proposes an architecture that can learn symbolic representations from the continuous sensorimotor experience of a robot interacting with a varying number of objects. Unlike previous works, this work aims to ...
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