English(EN) Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts

深度学习模型预测软体机器人中的粘附力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-22 04:00

研究人员开发了一种深度学习模型，能够快速预测粘弹性材料中的粘附力，这项任务以前需要计算密集型模拟。该模型利用带有LSTM网络的序列到序列架构，可以根据规定的位移历史预测完整的力轨迹。这种方法显著减少了计算时间，使其适用于软体机器人和操纵任务中的实时应用。 AI

影响为软体机器人应用实现更快的设计和实时控制。

排序理由学术论文，详细介绍了一种用于预测材料力的新深度学习模型。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 学术论文，详细介绍了一种用于预测材料力的新深度学习模型。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.AI TIER_1 English(EN) · Ali Maghami, Merten Stender, Michele Ciavarella, Antonio Papangelo · 2026-07-22 04:00

基于深度学习的粘弹性赫兹接触中时变粘附力的预测

arXiv:2607.19060v1 Announce Type: cross Abstract: 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 …