Enabling the Sense of Self in a Dual-Arm Robot

TL;DR

This paper introduces a neural network architecture that enables a dual-arm robot to develop a sense of self, distinguishing its limbs from the environment using visual and proprioceptive data, crucial for autonomous task execution.

Contribution

The paper presents a novel neural network model inspired by human self-awareness development, allowing robots to recognize their limbs in complex environments.

Findings

Achieved 88.7% accuracy in self-recognition in cluttered settings

Demonstrated robustness against confounding input signals

Enabled a robot to differentiate itself from surroundings

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.