# Reinforcement Learning Control of a Forestry Crane Manipulator

arXiv: https://arxiv.org/abs/2103.02315 · HTML: https://mlsanity.com/paper/2103.02315/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2021.

## Built on by (most cited first, 6 of 6)

- [Instance Segmentation for Autonomous Log Grasping in Forestry Operations](https://mlsanity.com/paper/2203.01902/) (2022)
- [Reinforcement Learning Control for Autonomous Hydraulic Material Handling Machines with Underactuated Tools](https://mlsanity.com/paper/2410.05093/) (2024)
- [GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control](https://mlsanity.com/paper/2202.13638/) (2022)
- [Automatic Evaluation of Excavator Operators using Learned Reward Functions](https://mlsanity.com/paper/2211.07941/) (2022)
- [Towards Reinforcement Learning Based Log Loading Automation](https://mlsanity.com/paper/2510.26363/) (2025)
- [Residual Reinforcement Learning for Waste-Container Lifting Using Large-Scale Cranes with Underactuated Tools](https://mlsanity.com/paper/2602.05895/) (2026)

## Abstract (arXiv)

Forestry machines are heavy vehicles performing complex manipulation tasks in unstructured production forest environments. Together with the complex dynamics of the on-board hydraulically actuated cranes, the rough forest terrains have posed a particular challenge in forestry automation. In this study, the feasibility of applying reinforcement learning control to forestry crane manipulators is investigated in a simulated environment. Our results show that it is possible to learn successful actuator-space control policies for energy efficient log grasping by invoking a simple curriculum in a deep reinforcement learning setup. Given the pose of the selected logs, our best control policy reaches a grasping success rate of 97%. Including an energy-optimization goal in the reward function, the energy consumption is significantly reduced compared to control policies learned without incentive for energy optimization, while the increase in cycle time is marginal. The energy-optimization effects can be observed in the overall smoother motion and acceleration profiles during crane manipulation.