基于结构-感知-学习的软夹持器的松弛感知多模态感知
Relaxation-Aware Multimodal Sensing of Soft Gripper Driven by Structure-Perception-Learning
浏览论文内容
中文总结 AI 辅助
该研究针对软夹持器抓取力因粘弹性松弛衰减的问题，提出结构-感知-学习框架，结合变刚度设计与温度耦合粘弹性力表征，使280秒力控抓取误差降低80%至95%，实现稳定持续抓取。
中文摘要 AI 辅助
实现软机械臂的稳定、持续抓取仍是一项基础挑战。柔性使接触安全且自适应，但软聚合物的固有粘弹性会导致应力松弛，保持抓取时抓取力持续衰减。受人类抓取（结合相位相关刚度调节与连续感知和反馈）启发，本文提出一种集成的结构-感知-学习（structure-perception-learning）框架。我们研发了一种变刚度软夹持器，其使用机载视觉和红外热成像实时跟踪变形与温度场，可持续跟踪交互状态。为缓解松弛引发的力衰减，我们提出一种温度耦合粘弹性力表征，结合物理信息学习模型，以重构力趋势并在保持抓取时提供明确补偿。实验表明，在280秒的力控制抓取保持任务中，所提方法维持期望力的平均绝对误差为0.066N，分别比固定孔径基线和仅瞬时基线的性能提升80%和95%。总体而言，结果支持一种机构-AI协同设计观点：机构塑造可行的交互，而学习补偿粘弹性动力学中的剩余不确定性，共同实现稳定、持续的抓取。
英文摘要
Achieving stable, sustained grasping with soft robotic hands remains a fundamental challenge. Compliance enables safe and adaptive contact, yet the intrinsic viscoelasticity of soft polymers leads to stress relaxation and a continuous decay of grasping force during holding. Inspired by human grasping, which combines phase-dependent stiffness regulation with continuous sensing and feedback, this paper presents an integrated structure--perception--learning framework. We develop a variable-stiffness soft gripper that uses onboard vision and infrared thermography to track deformation and the temperature field in real time, preserving continuous tracking of the interaction state. To mitigate relaxation-induced force decay, we propose a temperature-coupled viscoelastic force representation, together with a physics-informed learning model, to reconstruct the force trend and provide explicit compensation during holding. Experiments show that, in a 280s force-controlled grasp-and-hold task, the proposed method maintains the desired force with a mean absolute error of 0.066N, outperforming fixed-aperture and instantaneous-only baselines by 80% and 95%, respectively. Overall, the results support a mechanism--AI co-design view: mechanisms shape feasible interactions, while learning compensates remaining uncertainty in viscoelastic dynamics, together enabling stable, sustained grasping.
发表机构
- School of Mechanical Engineering, Zhejiang University（浙江大学机械工程学院）
- Torch Kernel Co., Ltd.（炬芯科技股份有限公司）
机构由 AI 辅助整理，请以论文原文为准。
