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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 ...
Developmental scaffolding with large language models
(IEEE, 2023)
Exploration and self-observation are key mechanisms of infant sensorimotor development. These processes are further guided by parental scaffolding to accelerate skill and knowledge acquisition. In developmental robotics, ...
Interplay between neural computational energy and multimodal processing in robot-robot interaction
(IEEE, 2023)
Multimodal learning is an active research area that is gaining importance in human-robot interaction. Despite the obvious benefit of levering multiple sensors for perceiving the world, its neural computational cost has not ...
A model for cognitively valid lifelong learning
(IEEE, 2023)
In continual learning, usually a sequence of tasks are given to a learning agent and the performance of the agent after learning is measured in terms of resistance to catastrophic forgetting, efficacy of knowledge transfer ...
Advancing humanoid robots for social integration: Evaluating trustworthiness through a social cognitive framework
(IEEE, 2023)
Trust is an essential concept for human-human and human-robot interactions. Yet only a few studies have addressed this concept from a robot perspective - that is, forming robot trust in interaction partners. Our previous ...
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