A Learning System for Deformable Object Cooperative Manipulation

Donghao Shi

doi:10.1109/TASE.2024.3486063

A Learning System for Deformable Object Cooperative Manipulation

2024-01-01

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AI

DOI：10.1109/TASE.2024.3486063

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Dynamic motion primitives (DMPs) have been widely used in robotics and automation systems because of their rapid deployment capability. Previous research has concentrated on extending coupled dynamic movement primitives (CDMPs) to manipulate rigid objects using a dual-arm robot. However, manipulating deformable objects may fail due to issues with the workspace, manipulability caused by the robot's layout, and the uncertain dynamics of deformable objects. This research proposes a unique system that combines Learning from Demonstration (LfD) to optimise robot layouts based on human experience and robot manipulability. It also employs a modified CDMPs (MCDMPs) method to manipulate deformable objects. The MCDMPs include a new term to ensure that the deformed object can change its configuration during manipulation. Furthermore, another term is introduced to track the desired trajectory of the deformed object, which is crucial for transporting tasks. We conducted simulations using a mass-spring-damper system for cooperative manipulation to validate the proposed approach. We also employed a dual-arm robot platform to transport a deformed ball with disturbance. The simulation and experimental findings indicate that our method performs well in trajectory tracking and configuration change.

Keyword:

Deformable object manipulation

dynamic movement primitives (DMPs)

dual-arm robot

A Learning System for Deformable Object Cooperative Manipulation

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