通过温度耦合建模补偿软夹爪力衰减，实现稳定持久抓握。
Relaxation-Aware Multimodal Sensing of Soft Gripper Driven by Structure-Perception-Learning
- 融合视觉与红外测温，实时感知夹爪形变和温度场变化。
- 在280秒抓持中均方误差仅0.066N，优于基线80%以上。
- 适合需要长期稳定抓握的柔性机器人应用场景。
实现软体机械手的稳定持续抓握仍是根本挑战。柔顺性虽能实现安全自适应接触，但软聚合物固有的粘弹性导致应力松弛，抓持力持续衰减。受人类抓握启发，本文提出结构-感知-学习一体化框架。设计可变刚度软夹爪，利用机载视觉与红外热成像实时追踪形变及温度场，保持交互状态连续监测。为缓解松弛引起的力衰减，提出温度耦合的粘弹性力表征方法，并构建物理信息学习模型，重建力变化趋势并提供显式补偿。实验表明，在280秒力控抓持任务中，该方法维持目标力的平均绝对误差仅为0.066N，较固定孔径与仅瞬时感知基线分别提升80%和95%。结果支持机制与AI协同设计观点：机制塑造可行交互，学习补偿粘弹性动力学中的剩余不确定性，共同实现稳定持续抓握。
原文摘要 · Abstract (English)
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.
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