# Towards Reinforcement Learning Based Log Loading Automation

arXiv: https://arxiv.org/abs/2510.26363 · HTML: https://mlsanity.com/paper/2510.26363/

## Key facts

- No citing papers in the map yet (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 15 of its 31 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## 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, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://mlsanity.com/paper/2106.02039/) (2021)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Reverse Curriculum Generation for Reinforcement Learning](https://mlsanity.com/paper/1707.05300/) (2017)
- [A Review of Reinforcement Learning in Financial Applications](https://mlsanity.com/paper/2411.12746/) (2024)
- [Using Reinforcement Learning for Load Testing of Video Games](https://mlsanity.com/paper/2201.06865/) (2022)
- [Reinforcement Learning Control of a Forestry Crane Manipulator](https://mlsanity.com/paper/2103.02315/) (2021)
- [Control of rough terrain vehicles using deep reinforcement learning](https://mlsanity.com/paper/2107.01867/) (2021)
- [Multi-log grasping using reinforcement learning and virtual visual servoing](https://mlsanity.com/paper/2309.02997/) (2023)
- [Autonomous Control of Redundant Hydraulic Manipulator Using Reinforcement Learning with Action Feedback](https://mlsanity.com/paper/2504.15714/) (2025)

## Abstract (arXiv)

Forestry forwarders play a central role in mechanized timber harvesting by picking up and moving logs from the felling site to a processing area or a secondary transport vehicle. Forwarder operation is challenging and physically and mentally exhausting for the operator who must control the machine in remote areas for prolonged periods of time. Therefore, even partial automation of the process may reduce stress on the operator. This study focuses on continuing previous research efforts in application of reinforcement learning agents in automating log handling process, extending the task from grasping which was studied in previous research to full log loading operation. The resulting agent will be capable to automate a full loading procedure from locating and grappling to transporting and delivering the log to a forestry forwarder bed. To train the agent, a trailer type forestry forwarder simulation model in NVIDIA's Isaac Gym and a virtual environment for a typical log loading scenario were developed. With reinforcement learning agents and a curriculum learning approach, the trained agent may be a stepping stone towards application of reinforcement learning agents in automation of the forestry forwarder. The agent learnt grasping a log in a random position from grapple's random position and transport it to the bed with 94% success rate of the best performing agent.