Relaxation-Aware Multimodal Sensing of Soft Gripper Driven by Structure-Perception-Learning

• URL: http://arxiv.org/abs/2608.26622v1
• Date: Thu, 27 Aug 2026 05:19:02 GMT
• Title: Relaxation-Aware Multimodal Sensing of Soft Gripper Driven by Structure-Perception-Learning
• Abstract summary: The intrinsic viscoelasticity of soft polymers leads to stress relaxation and a continuous decay of grasping force during holding.<n>Inspired by human grasping, this paper presents an integrated structure--perception--learning framework.<n>We develop a variable-stiffness soft gripper that uses onboard vision and infrared thermography to track deformation and the temperature field in real time.
• Score: 4.3068545796037325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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