Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach

• URL: http://arxiv.org/abs/2607.15656v1
• Date: Fri, 17 Jul 2026 06:04:08 GMT
• Title: Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach
• Abstract summary: This paper proposes a simulation-to-real framework for learning a system-level digital surrogate.<n>Instead of modeling internal dynamics, the excavator is treated as an input-output operator.<n>The proposed model can serve as a drop-in surrogate for both simulation and physical systems.
• Score: 19.924870252624647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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