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| Main Authors: | , |
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| Format: | Preprint |
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2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2011.07026 |
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| _version_ | 1866929565114302464 |
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| author | AlQallaf, Ali Aragon-Camarasa, Gerardo |
| author_facet | AlQallaf, Ali Aragon-Camarasa, Gerardo |
| contents | 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% on average in cluttered environmental settings and under confounding input signals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2011_07026 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Enabling the Sense of Self in a Dual-Arm Robot AlQallaf, Ali Aragon-Camarasa, Gerardo Robotics Artificial Intelligence 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% on average in cluttered environmental settings and under confounding input signals. |
| title | Enabling the Sense of Self in a Dual-Arm Robot |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2011.07026 |