发表机构
Stevens Institute of Technology（史蒂文斯理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在开发强力交互时抓握工具行为的预测模型，引入分析模型驱动神经网络（AMINN），结合分析力学层与数据驱动学习，在三指欠驱动机器人手上评估，有强预测能力且实现更好物理一致性，推动了物理可解释学习。
AI 中文摘要
在机器人操作研究中，抓握常被视为二元的成功或失败问题。但对于强力工具使用，这种观点不足，抓握顺应性是关键因素。顺应性源于多种因素产生非线性行为，理解其关系对预测模型很重要。在欠驱动手中此效应更明显。我们的目标是开发强力交互时抓握工具行为的预测模型。为此引入分析模型驱动神经网络（AMINN），它结合分析力学层和数据驱动学习来估计抓握稳定性等。在三指欠驱动机器人手上评估，该模型有强预测能力，与黑箱多层感知器基线相比，还实现更好的基于能量的物理一致性。此框架推动了机器人操作中物理可解释学习，支持更可靠、安全和值得信赖的自主工具使用。
英文摘要
In robotic manipulation studies, grasping is often treated as a binary success or failure problem, usually defined by whether the object simply stays in the hand. For forceful tool use, however, this view is insufficient because grasp compliance becomes a critical factor governing how the hand and tool behave under load. Compliance arises from coupled kinematics, grasp configuration, passive mechanics, and contact conditions, producing nonlinear behavior in which deformation and interaction forces influence each other. Understanding this relationship is essential for predictive models of how a grasped tool and a compliant hand jointly respond to external loading. In underactuated hands, these effects are amplified: such designs offer low cost and adaptive grasping, but make compliance behavior more difficult to model and predict. Our goal is therefore to develop a predictive model for grasped tool behavior during forceful interactions. To address this challenge, we introduce an analytical model informed neural network (AMINN), a hybrid predictive model that combines an analytical mechanics layer with data driven learning to estimate grasp stability and in hand tool displacement under external loading. The model is evaluated on a three finger underactuated robotic hand and shows strong predictive capability with mechanically meaningful outputs across diverse loading conditions. Compared with a black box multilayer perceptron baseline, AMINN also achieves better energy based physical consistency. Beyond prediction accuracy alone, this framework advances physically interpretable learning for robotic manipulation and supports more reliable, safer, and more trustworthy autonomous tool use in safety critical settings during forceful interaction.
CommentsPreprint. 9 pages, 8 figures, 1 table
