虽然人类意识到自己的身体和能力，但机器人却没有。为了解决这个问题，我们在本文中提出了一种神经网络架构，使双臂机器人能够在环境中获得自我感觉。我们的方法受到人类自我意识发展水平的启发，并作为机器人在环境中执行任务时实现自我意识的基础构件。我们假设机器人在与环境交互之前必须了解自己，以便能够支持不同的机器人任务。因此，我们实现了一个神经网络架构，使机器人能够使用视觉和本体感觉输入来区分其肢体和环境。我们通过实验证明，在混乱的环境和混杂的输入信号下，机器人能够以平均88.7%的准确率区分自己。

原文题目：Enabling the Sense of Self in a Dual-Arm Robot

原文：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 dualarm 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.

原文作者：Ali AlQallaf，Gerardo Aragon-Camarasa

原文地址:https://arxiv.org/abs/2011.07026