Spring-IMU Fusion Based Proprioception for Feedback Control of Soft Manipulators

TL;DR

This paper introduces a geometry-based sensor fusion framework using IMUs and inductive springs, combined with machine learning, to enable robust proprioception and closed-loop control in soft manipulators, achieving high accuracy and adaptability.

Contribution

It presents a novel sensor fusion and machine learning approach for proprioception and control in soft robots, with effective sim-to-real transfer and robust performance.

Findings

Achieved 0.7% average error in pose estimation across the workspace.

Demonstrated successful path following and pick-and-place tasks under external loads.

Developed a gradient descent-based control algorithm for soft manipulator positioning.

Abstract

This paper presents a novel framework to realize proprioception and closed-loop control for soft manipulators. Deformations with large elongation and large bending can be precisely predicted using geometry-based sensor signals obtained from the inductive springs and the inertial measurement units (IMUs) with the help of machine learning techniques. Multiple geometric signals are fused into robust pose estimations, and a data-efficient training process is achieved after applying the strategy of sim-to-real transfer. As a result, we can achieve proprioception that is robust to the variation of external loading and has an average error of 0.7% across the workspace on a pneumatic-driven soft manipulator. The realized proprioception on soft manipulator is then contributed to building a sensor-space based algorithm for closed-loop control. A gradient descent solver is developed to drive the end-effector to achieve the required poses by iteratively computing a sequence of reference sensor signals. A conventional controller is employed in the inner loop of our algorithm to update actuators (i.e., the pressures in chambers) for approaching a reference signal in the sensor-space. The systematic function of closed-loop control has been demonstrated in tasks like path following and pick-and-place under different external loads.