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Deepsym: Deep symbol generation and rule learning for planning from unsupervised robot interaction
(AI Access Foundation, 2022)
Symbolic planning and reasoning are powerful tools for robots tackling complex tasks. However, the need to manually design the symbols restrict their applicability, especially for robots that are expected to act in open-ended ...
Multimodal reinforcement learning for partner specific adaptation in robot-multi-robot interaction
(IEEE, 2022)
Successful and efficient teamwork requires knowledge of the individual team members' expertise. Such knowledge is typically acquired in social interaction and forms the basis for socially intelligent, partner-Adapted ...
Adaptive shared control with human intention estimation for human agent collaboration
(IEEE, 2022)
In this paper an adaptive shared control frame-work for human agent collaboration is introduced. In this framework the agent predicts the human intention with a confidence factor that also serves as the control blending ...
High-level features for resource economy and fast learning in skill transfer
Abstraction is an important aspect of intelligence which enables agents to construct robust representations for effective and efficient decision making. Although, deep neural networks are proven to be effective learning ...
Trustworthiness assessment in multimodal human-robot interaction based on cognitive load
(IEEE, 2022)
In this study, we extend our robot trust model into a multimodal setting in which the Nao robot leverages audio-visual data to perform a sequential multimodal pattern recalling task while interacting with a human partner ...
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 ...
Trust in robot–robot scaffolding
(IEEE, 2023-12-01)
The study of robot trust in humans and other agents is not explored widely despite its importance for the near future human-robot symbiotic societies. Here, we propose that robots should trust partners that tend to reduce ...
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 ...
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