Enabling the Sense of Self in a Dual-Arm Robot

• URL: http://arxiv.org/abs/2011.07026v1
• Date: Fri, 13 Nov 2020 17:25:07 GMT
• Title: Enabling the Sense of Self in a Dual-Arm Robot
• Authors: Ali AlQallaf, Gerardo Aragon-Camarasa
• Abstract summary: We present a neural network architecture that enables a dual-arm robot to get a sense of itself in an environment. We demonstrate experimentally that a robot can distinguish itself with an accuracy of 88.7% on average in cluttered environmental settings.
• Score: 2.741266294612776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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