Simultaneous State Estimation and Online Model Learning in a Soft Robotic System

TL;DR

This paper presents a method for real-time state estimation and model learning in soft robots, combining a particle filter with Gaussian Processes to improve accuracy and predictability.

Contribution

It introduces a novel approach that simultaneously estimates the robot's pose and learns a bending stiffness model online using a gray-box system identification tool.

Findings

The method accurately estimates the robot's pose in real-time.

It learns a bending stiffness model online that enhances prediction accuracy.

Reduced error in multi-step predictions demonstrates improved model quality.

Abstract

Operating complex real-world systems, such as soft robots, can benefit from precise predictive control schemes that require accurate state and model knowledge. This knowledge is typically not available in practical settings and must be inferred from noisy measurements. In particular, it is challenging to simultaneously estimate unknown states and learn a model online from sequentially arriving measurements. In this paper, we show how a recently proposed gray-box system identification tool enables the estimation of a soft robot's current pose while at the same time learning a bending stiffness model. For estimation and learning, we only need a nominal constant-curvature robot model and measurements of the robot's base reactions (e.g., base forces). The estimation scheme -- relying on a marginalized particle filter -- allows us to conveniently interface nominal constant-curvature equations with a Gaussian Process (GP) bending stiffness model to be learned. This, in contrast to estimation via a random walk over stiffness values, enables prediction of bending stiffness and improves overall model quality. We demonstrate, using a real-world soft robot, that the method learns a bending-stiffness model online while accurately estimating the robot's pose. Notably, reduced error in multi-step forward predictions indicates that the learned bending-stiffness GP improves overall model quality.