緩和を考慮したソフトグリッパのマルチモーダルセンシング：構造・知覚・学習の統合
Relaxation-Aware Multimodal Sensing of Soft Gripper Driven by Structure-Perception-Learning
ソフトグリッパの粘弾性による把持力低下を、温度と変形のリアルタイム計測と物理情報学習モデルで補償し、長時間の安定把持を実現した。
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著者: Yanzhe Wang, Hao Wu, Ziyi Zheng, Huixu Dong
分類: cs.RO
原文アブストラクト
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.