Dynamic Cloth Manipulation Considering Variable Stiffness and Material Change Using Deep Predictive Model with Parametric Bias

TL;DR

This paper presents a deep predictive model enabling a humanoid robot with variable stiffness to dynamically manipulate cloth and adapt to material changes, inspired by human skillful handling of flexible objects.

Contribution

It introduces a novel deep predictive model that integrates variable stiffness control and parametric bias to handle cloth manipulation and material changes.

Findings

Successful simulation and robot experiments demonstrating dynamic cloth manipulation.

Effective detection and adaptation to material property changes during manipulation.

Enhanced manipulation capabilities with variable stiffness mechanisms.

Abstract

Dynamic manipulation of flexible objects such as fabric, which is difficult to modelize, is one of the major challenges in robotics. With the development of deep learning, we are beginning to see results in simulations and in some actual robots, but there are still many problems that have not yet been tackled. Humans can move their arms at high speed using their flexible bodies skillfully, and even when the material to be manipulated changes, they can manipulate the material after moving it several times and understanding its characteristics. Therefore, in this research, we focus on the following two points: (1) body control using a variable stiffness mechanism for more dynamic manipulation, and (2) response to changes in the material of the manipulated object using parametric bias. By incorporating these two approaches into a deep predictive model, we show through simulation and actual robot experiments that Musashi-W, a musculoskeletal humanoid with variable stiffness mechanism, can dynamically manipulate cloth while detecting changes in the physical properties of the manipulated object.