Remote Manipulation of Multiple Objects with Airflow Field Using Model-Based Learning Control

TL;DR

This paper presents a model-based learning control method for remotely manipulating multiple objects using airflow, enabling precise control at meter-scale distances across various tasks.

Contribution

It introduces an analytical airflow model combined with learned object dynamics and a model-based controller for remote multi-object manipulation.

Findings

Effective remote control of objects at meter-scale distances.

Successful manipulation tasks including path-following, aggregating, and sorting.

Robustness across different object types and configurations.

Abstract

Non-contact manipulation is a promising methodology in robotics, offering a wide range of scientific and industrial applications. Among the proposed approaches, airflow stands out for its ability to project across considerable distances and its flexibility in actuating objects of varying materials, sizes, and shapes. However, predicting airflow fields at a distance-and the motion of objects within them-remains notoriously challenging due to their nonlinear and stochastic nature. Here, we propose a model-based learning approach using a jet-induced airflow field for remote multi-object manipulation on a surface. Our approach incorporates an analytical model of the field, learned object dynamics, and a model-based controller. The model predicts an air velocity field over an infinite surface for a specified jet orientation, while the object dynamics are learned through a robust system identification algorithm. Using the model-based controller, we can automatically and remotely, at meter-scale distances, control the motion of single and multiple objects for different tasks, such as path-following, aggregating, and sorting.