This is not the latest version of this item. The latest version can be found at: https://www.research-collection.ethz.ch/handle/20.500.11850/475773
- Conference Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
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. Show more
Book title2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Pages / Article No.
SubjectHydraulic Excavator, Reinforcement Learning
Organisational unit09570 - Hutter, Marco / Hutter, Marco
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication
NotesDue to the Coronavirus (COVID-19) the conference was conducted virtually.
MoreShow all metadata