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

arXiv:2607.15656v1 Announce Type: new 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 und...