A Learning System for Deformable Object Cooperative Manipulation

摘要

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. Note to Practitioners—This research paper addresses the need for faster deployment of dual-arm robots in various applications, such as industrial and services. We must consider the robot’s installation layout and task programming for rapid deployment. To optimise layouts for tasks quickly, we introduce an optimisation framework which considers the comprehensive performance of dual arms and uses demonstration sampling and learning generalisation. To address the manipulation of deformable objects by dual-arm robots, we use an MCDMPs method that employs the barrier Lyapunov function (BLF) to track changed configurations. Additionally, we introduce a term for reference trajectory tracking component for the manipulated object.