Learning the Approach During the Short-loading Cycle Using Reinforcement Learning
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
The short-loading cycle is a repetitive task performed in high quantities, making it a great alternative for automation. In the short-loading cycle, an expert operator navigates towards a pile, fills the bucket with material, navigates to a dump truck, and dumps the material into the tipping body. The operator has to balance the productivity goal while minimising the fuel usage, to maximise the overall efficiency of the cycle. In addition, difficult interactions, such as the tyre-to-surface interaction further complicate the cycle. These types of hard-to-model interactions that can be difficult to address with rule-based systems, together with the efficiency requirements, motivate us to examine the potential of data-driven approaches. In this paper, the possibility of teaching an agent through reinforcement learning to approach a dump truck's tipping body and get in position to dump material in the tipping body is examined. The agent is trained in a 3D simulated environment to perform a simplified navigation task. The trained agent is directly transferred to a real vehicle, to perform the same task, with no additional training. The results indicate that the agent can successfully learn to navigate towards the dump truck with a limited amount of control signals in simulation and when transferred to a real vehicle, exhibits the correct behaviour.
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 7 of its 28 references that are in the map. First posted on arXiv 19 Jun 2024.
Builds on
7 of 28 references.
References · 7
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
- 2016OpenAI Gymcited by 1,736
- 2021Continuous control of an underground loader using deep reinforcement learningcited by 3
- 2023Understanding URDF: A Survey Based on User Experiencecited by 3
- 2022Autonomous Wheel Loader Trajectory Tracking Control Using LPV-MPCcited by 2
- 2019Sub-optimal Control of Autonomous Wheel loader with Approximate Dynamic Programmingcited by 1
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- 2025Robust Dynamic Material Handling via Adaptive Constrained Evolutionary Reinforcement Learning
- 2023World Modeling for Autonomous Wheel Loaders
- 2022Accelerating Reinforcement Learning for Autonomous Driving using Task-Agnostic and Ego-Centric Motion Skills
- 2024Autonomous Algorithm for Training Autonomous Vehicles with Minimal Human Intervention
- 2024Path Following and Stabilisation of a Bicycle Model using a Reinforcement Learning Approach
- 2025Large Scale Robotic Material Handling: Learning, Planning, and Control