Towards RL-Based Hydraulic Excavator Automation
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Date
2020
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
In this article we present a data-driven approach for automated arm control of a hydraulic excavator. Except for the link lengths of the excavator, our method does not require machine-specific knowledge nor gain tuning. Using data collected during operation of the excavator, we train a general purpose model to effectively represent the highly non-linear dynamics of the hydraulic actuation and joint linkage. Together with the link lengths a simulation is set up to train a neural network control policy for end-effector position tracking using reinforcement learning (RL). The control policy directly outputs the actuator commands that can be applied to the machine without unfounded filtering or modification. The proposed method is implemented and tested on a 12t hydraulic excavator, controlling its 4 main arm joints to track desired positions of the shovel in free-space. The results demonstrate the feasibility of directly applying control policies trained in simulation to the physical excavator for accurate and stable position tracking.
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Publication status
published
Editor
Book title
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Journal / series
Volume
Pages / Article No.
2692 - 2697
Publisher
IEEE
Event
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020) (virtual)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Hydraulic Excavator; Reinforcement Learning
Organisational unit
09570 - Hutter, Marco / Hutter, Marco
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.
Funding
Related publications and datasets
Is part of: 10.3929/ethz-b-000487440