# Reinforcement Learning Control for Autonomous Hydraulic Material Handling Machines with Underactuated Tools

arXiv: https://arxiv.org/abs/2410.05093 · HTML: https://mlsanity.com/paper/2410.05093/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Graph-based Multi-sensor Fusion for Consistent Localization of Autonomous Construction Robots](https://mlsanity.com/paper/2203.01389/) (2022)
- [Reinforcement Learning Control of a Forestry Crane Manipulator](https://mlsanity.com/paper/2103.02315/) (2021)
- [HEAP -- The autonomous walking excavator](https://mlsanity.com/paper/2106.05059/) (2021)
- [Real-Time Motion Planning of a Hydraulic Excavator using Trajectory Optimization and Model Predictive Control](https://mlsanity.com/paper/2107.02366/) (2021)
- [GOMP-FIT: Grasp-Optimized Motion Planning for Fast Inertial Transport](https://mlsanity.com/paper/2110.15326/) (2021)
- [Towards Autonomous Excavation Planning](https://mlsanity.com/paper/2308.11478/) (2023)

## Built on by (most cited first, 4 of 4)

- [Holistic Fusion: Task- and Setup-Agnostic Robot Localization and State Estimation with Factor Graphs](https://mlsanity.com/paper/2504.06479/) (2025)
- [Residual Reinforcement Learning for Waste-Container Lifting Using Large-Scale Cranes with Underactuated Tools](https://mlsanity.com/paper/2602.05895/) (2026)
- [Contraction Metric Based Safe Reinforcement Learning Force Control for a Hydraulic Actuator with Real-World Training](https://mlsanity.com/paper/2602.08977/) (2026)
- [Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks](https://mlsanity.com/paper/2604.09487/) (2026)

## Abstract (arXiv)

The precise and safe control of heavy material handling machines presents numerous challenges due to the hard-to-model hydraulically actuated joints and the need for collision-free trajectory planning with a free-swinging end-effector tool. In this work, we propose an RL-based controller that commands the cabin joint and the arm simultaneously. It is trained in a simulation combining data-driven modeling techniques with first-principles modeling. On the one hand, we employ a neural network model to capture the highly nonlinear dynamics of the upper carriage turn hydraulic motor, incorporating explicit pressure prediction to handle delays better. On the other hand, we model the arm as velocity-controllable and the free-swinging end-effector tool as a damped pendulum using first principles. This combined model enhances our simulation environment, enabling the training of RL controllers that can be directly transferred to the real machine. Designed to reach steady-state Cartesian targets, the RL controller learns to leverage the hydraulic dynamics to improve accuracy, maintain high speeds, and minimize end-effector tool oscillations. Our controller, tested on a mid-size prototype material handler, is more accurate than an inexperienced operator and causes fewer tool oscillations. It demonstrates competitive performance even compared to an experienced professional driver.