Continuum-Body-Pose Estimation From Partial Sensor Information Using Recurrent Neural Networks

Abstract

Soft continuum arms have significant potential for use in various applications due to their extremely high degrees of freedom. For example, these soft arms can be used for grasping and manipulating fragile materials in the deep sea or carrying a human to rescue in unstructured environments. However, in these situations, the environment is often dark and visual cues are not always usable. Therefore, these arms must estimate their pose from proprioceptive sensors to control their behavior and execute their tasks in dark places. Estimating the pose in a dynamic situation is still challenging because of the arms' high dimensionality and the complex structural changes in the body shape. Therefore, this study demonstrates a novel method for estimating the pose of proprioceptive bending sensors using recurrent neural networks (RNNs). In particular, an RNN framework known as deep reservoir computing was used for this purpose. Results from experiments using an octopus-inspired soft robotic arm clearly indicate that the proposed method significantly outperforms existing methods using long short-term memory models or linear models. We expect that our proposed method will enable behavioral control of these arms in dark places such as the deep sea, space, and inside the human body in future applications.