Enabling the Sense of Self in a Dual-Arm Robot
This addresses the challenge of robot self-awareness for improved interaction and task performance in robotics, though it appears incremental as it builds on human developmental models.
The paper tackles the problem of enabling robots to have a sense of self-awareness, similar to humans, by developing a neural network architecture that allows a dual-arm robot to differentiate its limbs from the environment using visual and proprioception inputs, achieving an average accuracy of 88.7% in cluttered settings.
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.