发表机构
National Engineering Research Center of Mobile Network Technologies, Beijing University of Posts and Telecommunications; Yaowu (Shenzhen) Technology Co., Ltd.（北京邮电大学移动网络技术国家工程研究中心; 耀武（深圳）科技有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对液压挖掘机开发受限问题，提出用LSTM网络学习系统级数字代理的模拟到现实框架，经模拟验证和实际移植，引入状态估计方法解决数据问题，实验表明该代理能高保真重现相关行为，助力挖掘自动化算法开发。
AI 中文摘要
开发自主液压挖掘机受限于实体机器获取有限和实际实验成本高昂。本文提出一种从模拟到现实的框架，使用长短期记忆（LSTM）网络学习系统级数字代理。将挖掘机视为输入输出算子而非对内部动力学建模，训练代理在相同控制输入下重现其闭环行为。该方法先在MuJoCo模拟环境验证，再移植到真实挖掘机。引入基于自适应卡尔曼滤波的一致性感知状态估计方法解决实际数据测量不一致问题。实验结果表明，学习到的代理在闭环自回归评估下，在角速度和长时轨迹重现方面实现高保真。这些结果证实所提模型可作为模拟和物理系统的直接替代，实现挖掘自动化算法的可扩展高效开发。
英文摘要
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.
