Dynamic Throwing with Robotic Material Handling Machines

TL;DR

This paper presents a reinforcement learning approach to enable hydraulic material handling machines to perform dynamic throwing motions, improving efficiency and reachability in logistics tasks through simulation and real-world experiments.

Contribution

It introduces a novel RL-based method for controlling underactuated hydraulic arms to perform dynamic throwing, exploiting passive joints for enhanced performance.

Findings

Successfully demonstrated dynamic throwing in simulation and real-world tests

Achieved accurate object targeting outside static reachability zones

Showed potential for improving efficiency in autonomous material handling

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.