Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach
Robotics
Summary
The authors created a computer model that can copy how a hydraulic excavator moves by learning from its input and output data, rather than trying to simulate all the tiny parts inside. They first tested their method in a simulated environment and then applied it to a real excavator. They also developed a technique to handle noisy real-world measurements. Their model accurately predicts the excavator's movements over time, making it useful as a replacement for both virtual and real machines. This helps researchers develop autonomous excavators more easily and cheaply.
Authors
Shuai Wang, Shen Wang, Qiang Wang, Muguo Du, Donghai Shi, Chenyu Wang, Xiaofeng Tao
Abstract
Developing autonomous hydraulic excavators is constrained by limited access to physical machines and the high cost of real-world experimentation. This paper proposes a simulation-to-real framework for learning a system-level digital surrogate using Long Short-Term Memory (LSTM) networks. Instead of modeling internal dynamics, the excavator is treated as an input-output operator, and the surrogate is trained to reproduce its closed-loop behavior under identical control inputs. The approach is first validated in a MuJoCo simulation environment and then transferred to a real excavator. To address measurement inconsistencies in real-world data, a consistency-aware state estimation method based on adaptive Kalman filtering is introduced. Experimental results demonstrate that the learned surrogate achieves high fidelity in both angular velocity and long-horizon trajectory reproduction under closed-loop autoregressive evaluation. These results confirm that the proposed model can serve as a drop-in surrogate for both simulation and physical systems, enabling scalable and efficient development of excavation automation algorithms.