发表机构
Chair of Cyber-Physical Systems in Mechanical Engineering, Technische Universität Berlin; TriboDynamics Lab, Department of Mechanics, Mathematics and Management, Polytechnic University of Bari（柏林工业大学机械工程系网络物理系统主席; 巴里理工大学力学、数学与管理系摩擦动力学实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对软机器人等领域中快速预测粘弹性接触响应的挑战，训练标量条件、序列到序列的深度学习模型，引入FMS表示，经多种架构训练比较，最佳模型有LSTM架构，能快速预测力轨迹，为数值评估提供替代，可用于控制应用。
AI 中文摘要
快速预测粘性软粘弹性接触的响应是软机器人技术以及抓取和操作任务中的当前挑战。确定完整的时间分辨力轨迹需要完整的数值模拟，其计算成本强烈依赖参数，不适用于实时应用或设计优化循环。在这项工作中，我们通过训练一个标量条件、有状态、序列到序列的深度学习模型来克服这一限制，该模型可根据规定的位移历史预测短程和长程粘附状态下的全力演变。数据集涵盖加载和卸载速率的四个数量级，并包括不同的停留时间，泰伯参数范围为0.2至3.2。为了在这些异构时间尺度上进行学习，我们引入了一种固定测量步长（FMS）表示，它将可变长度轨迹转换为固定长度序列，同时保留其物理时间信息。训练了不同的架构，包括长短期记忆（LSTM）网络、时间卷积神经网络（TCN）网络以及具有三种不同泰伯条件机制的时间分布密集层。使用全局波形和误差指标对模型进行比较。我们发现性能最佳的模型具有带级联条件的LSTM架构，其留出的均方误差为5.0×10⁻⁴，中位拉脱力误差约为2.2%，中位滞后误差约为1.1%。对于留出的协议，该模型预测完整的力轨迹，中位推理时间为0.16秒。该模型在未见参数组合上进行测试，并与分析极限情况进行对比，为重复数值评估提供了快速替代方案，具有在面向控制的应用中的潜在用途。
英文摘要
Fast prediction of the response of adhesive soft viscoelastic contacts represents a current challenge in soft robotics and for gripping and manipulation tasks. Determining the complete time-resolved force trajectory requires full numerical simulations, whose computational cost is strongly parameter-dependent, making them impractical for real-time application or design-optimization loops. In this work, we overcome this limitation by training a scalar-conditioned, stateful, sequence-to-sequence deep learning model to predict the full force evolution from a prescribed displacement history for both short- and long-range adhesion regimes. The data set spans four orders of magnitude in loading and unloading rates and includes varied dwell times, with the Tabor parameter ranging from $0.2$ to $3.2$. To enable learning across these heterogeneous time scales, we introduce a fixed-measurement-step (FMS) representation that converts variable-length trajectories into fixed-length sequences while preserving their physical-time information. Different architectures were trained, including long short-term memory (LSTM) networks, temporal convolutional neural (TCN) networks, and time-distributed dense layers with three different Tabor-conditioning mechanisms. The models were compared using global waveform and error metrics. We found that the best-performing model has an LSTM architecture with concatenated conditioning, which achieves a held-out mean-squared error of $5.0\times10^{-4}$, a median pull-off-force error of $\approx2.2\%$, and a median hysteresis error of $\approx1.1\%$. For the held-out protocols, the model predicts a complete force trajectory with a median inference time of $0.16$ s. The model is tested across unseen parameter combinations and against analytical limiting cases, providing a rapid surrogate for repeated numerical evaluations with potential use in control-oriented applications.
