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This paper addresses the challenge of stable grasping in soft robotic hands by integrating a structure-perception-learning framework that mimics human grasping techniques. The authors develop a variable-stiffness soft gripper equipped with real-time vision and infrared thermography to monitor deformation and temperature, allowing for continuous interaction state tracking. Their approach, which includes a temperature-coupled viscoelastic force representation and a physics-informed learning model, significantly reduces force decay during a 280s grasp-and-hold task, achieving a mean absolute error of only 0.066N and outperforming traditional methods by substantial margins.
A novel soft gripper design maintains stable grasping forces over time, achieving unprecedented accuracy by compensating for stress relaxation in real-time.
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.
