# Dynamic Throwing with Robotic Material Handling Machines

arXiv: https://arxiv.org/abs/2405.19001 · HTML: https://mlsanity.com/paper/2405.19001/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2024.

## Built on by (most cited first, 3 of 3)

- [Learning to Throw-Flip](https://mlsanity.com/paper/2510.10357/) (2025)
- [Residual Reinforcement Learning for Waste-Container Lifting Using Large-Scale Cranes with Underactuated Tools](https://mlsanity.com/paper/2602.05895/) (2026)
- [Non-Prehensile Throwing: A Reinforcement Learning Perspective](https://mlsanity.com/paper/2609.00771/) (2026)

## Abstract (arXiv)

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.