Bodily aware soft robots: integration of proprioceptive and exteroceptive sensors

TL;DR

This paper introduces a novel method for soft robots to develop bodily awareness by integrating sensory data through neural networks, enabling them to predict visual information and imagine their motion without visual input.

Contribution

It presents a new approach combining autoencoders and recurrent neural networks to enable bodily awareness in soft robots, bridging internal sensors and visual perception.

Findings

Soft robots can predict visual information from internal sensors.

Robots can imagine their motion without visual input.

The method enhances robotic bodily awareness capabilities.

Abstract

Being aware of our body has great importance in our everyday life. This is the reason why we know how to move in a dark room or to grasp a complex object. These skills are important for robots as well, however, robotic bodily awareness is still an unsolved problem. In this paper we present a novel method to implement bodily awareness in soft robots by the integration of exteroceptive and proprioceptive sensors. We use a combination of a stacked convolutional autoencoder and a recurrent neural network to map internal sensory signals to visual information. As a result, the simulated soft robot can learn to \textit{imagine} its motion even when its visual sensor is not available.