# Keep soft robots soft -- a data-driven based trade-off between feed-forward and feedback control

## TL;DR

This paper proposes a data-driven approach using Gaussian Process regression to balance feed-forward and feedback control in soft robots, enhancing safety and control accuracy despite model uncertainties.

## Contribution

It introduces a novel method that adaptively adjusts control gains based on model confidence, improving soft robot control without requiring precise models.

## Key findings

- Effective reduction of feedback gains in high-confidence regions
- Improved safety and control performance in soft robots
- Demonstrated robustness to model uncertainties

## Abstract

Tracking control for soft robots is challenging due to uncertainties in the system model and environment. Using high feedback gains to overcome this issue results in an increasing stiffness that clearly destroys the inherent safety property of soft robots. However, accurate models for feed-forward control are often difficult to obtain. In this article, we employ Gaussian Process regression to obtain a data-driven model that is used for the feed-forward compensation of unknown dynamics. The model fidelity is used to adapt the feed-forward and feedback part allowing low feedback gains in regions of high model confidence.