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Now showing items 31-40 of 47
An ecologically valid reference frame for perspective invariant action recognition
(IEEE, 2021)
In robotics, objects and body parts can be represented in various coordinate frames to ease computation. In biological systems, body or body part centered coordinate frames have been proposed as possible reference frames ...
Modeling robot trust based on emergent emotion in an interactive task
(IEEE, 2021)
Trust is an essential component in human-human and human-robot interactions. The factors that play potent roles in these interactions have been an attractive issue in robotics. However, the studies that aim at developing ...
Combined weight and density bounds on the polynomial threshold function representation of Boolean functions
(Elsevier, 2022-08)
In an earlier report it was shown that an arbitrary n-variable Boolean function f can be represented as a polynomial threshold function (PTF) with 0.75×2n or less number of monomials. In this report, we derive an upper ...
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 ...
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, ...
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