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An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction
During visuomotor tasks, robots have to compensate for the temporal dela...
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Bodily aware soft robots: integration of proprioceptive and exteroceptive sensors
Being aware of our body has great importance in our everyday life. This ...
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Deep Neural Object Analysis by Interactive Auditory Exploration with a Humanoid Robot
We present a novel approach for interactive auditory object analysis wit...
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Self-supervised Body Image Acquisition Using a Deep Neural Network for Sensorimotor Prediction
This work investigates how a naive agent can acquire its own body image ...
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Robot in the mirror: toward an embodied computational model of mirror self-recognition
Self-recognition or self-awareness is a capacity attributed typically on...
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Cognitively-inspired homeostatic architecture can balance conflicting needs in robots
Autonomous robots require the ability to balance conflicting needs, such...
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Utilizing Bluetooth Low Energy to recognize proximity, touch and humans
Interacting with humans is one of the main challenges for mobile robots ...
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Enabling the Sense of Self in a Dual-Arm Robot
While humans are aware of their body and capabilities, robots are not. To address this, we present in this paper a neural network architecture that enables a dual-arm robot to get a sense of itself in an environment. Our approach is inspired by human self-awareness developmental levels and serves as the underlying building block for a robot to achieve awareness of itself while carrying out tasks in an environment. We assume that a robot has to know itself before interacting with the environment in order to be able to support different robotic tasks. Hence, we implemented a neural network architecture to enable a robot to differentiate its limbs from the environment using visual and proprioception sensory inputs. We demonstrate experimentally that a robot can distinguish itself with an accuracy of 88.7 environmental settings and under confounding input signals.
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