Dynamic Throwing with Robotic Material Handling Machines
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Date
2024
Publication Type
Conference Paper
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yes
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Abstract
Automation of hydraulic material handling machinery is currently limited to semi-static pick-and-place cycles. Dynamic throwing motions which utilize the passive joints, can greatly improve time efficiency as well as increase the dumping workspace. In this work, we use Reinforcement Learning (RL) to design dynamic controllers for material handlers with underactuated arms as commonly used in logistics. The controllers are tested both in simulation and in real-world experiments on a 12-ton test platform. The method is able to exploit the passive joints of the gripper to perform dynamic throwing motions. With the proposed controllers, the machine is able to throw individual objects to targets outside the static reachability zone with good accuracy for its practical applications. The work demonstrates the possibility of using RL to perform highly dynamic tasks with heavy machinery, suggesting a potential for improving the efficiency and precision of autonomous material handling tasks.
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Publication status
published
Editor
Book title
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Journal / series
Volume
Pages / Article No.
98 - 104
Publisher
IEEE
Event
37th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)
Edition / version
Methods
Software
Geographic location
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Date created
Subject
Organisational unit
09570 - Hutter, Marco / Hutter, Marco
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication