论文标题
在双臂机器人中实现自我感
Enabling the Sense of Self in a Dual-Arm Robot
论文作者
论文摘要
尽管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，我们在本文中介绍了一种神经网络体系结构，使双臂机器人能够在环境中了解自己。我们的方法受到人类自我意识的发展水平的启发，是机器人在在环境中执行任务的同时实现自身意识的基础构建基础。我们假设机器人必须在与环境互动之前必须知道自己，以便能够支持不同的机器人任务。因此，我们实施了神经网络体系结构，以使机器人使用视觉和本体感受的感觉输入将其四肢与环境区分开。我们通过实验证明，在混乱的环境环境和混杂的输入信号下，机器人平均可以以平均88.7％的精度来区分自身。
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.
