Towards Reinforcement Learning Based Log Loading Automation
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
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.
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.
Builds on
15 of 31 references.
References · 15
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- 2025Towards Autonomous Wood-Log Grasping with a Forestry Crane: Simulator and Benchmarking ↗
- 2026Learning Grasp Targeting from Point Clouds for Log Pile Clearing on a Hydraulic Crane
- 2024Multi-agent Reinforcement Learning for Dynamic Dispatching in Material Handling Systems
- 20243D Operation of Autonomous Excavator based on Reinforcement Learning through Independent Reward for Individual Joints
- 2024Learning the Approach During the Short-loading Cycle Using Reinforcement Learning
- 2026Visuomotor Robotic Pruning in Planar Orchards Using Hybrid Reinforcement Learning
- 2026Size Doesn't Matter: Material-State Reinforcement Learning for Excavator Transferable Soil Manipulation
- 2026Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response