无需人类示范，通过模拟力控曲线实现机器人轻柔抓取
Learning Gentle Grasping from Human-Free Force Control Demonstration
- 用已知接触特性的物体自动生成理想力控曲线
- 双卷积网络结合物理力学模块，精准预测抓取力
- 适用于视觉触觉传感器，适合小样本下的稳定抓取
人类能基于触觉感知稳定而轻柔地抓取陌生物体。机器人因难以学习准确的抓取力预测与可泛化的力控策略，仍面临挑战。本文提出一种从理想力控示范中学习抓取的方法，在数据量有限的情况下实现类人手性能。利用具有已知接触特性的物体，自动生成参考力曲线，无需人工示范。设计双卷积神经网络（Dual-CNN）架构，融合物理力学模块，从示范中学习目标抓取力预测。该方法可有效应用于基于视觉的触觉传感器，实现从地面稳定、轻柔抓取物体。预测模型与抓取策略在离线评估与在线实验中验证，表现出高精度与强泛化能力。
原文摘要 · Abstract (English)
Humans can steadily and gently grasp unfamiliar objects based on tactile perception. Robots still face challenges in achieving similar performance due to the difficulty of learning accurate grasp-force predictions and force control strategies that can be generalized from limited data. In this article, we propose an approach for learning grasping from ideal force control demonstrations, to achieve similar performance of human hands with limited data size. Our approach utilizes objects with known contact characteristics to automatically generate reference force curves without human demonstrations. In addition, we design the dual convolutional neural networks (Dual-CNN) architecture which incorporats a physics-based mechanics module for learning target grasping force predictions from demonstrations. The described method can be effectively applied in vision-based tactile sensors and enables gentle and stable grasping of objects from the ground. The described prediction model and grasping strategy were validated in offline evaluations and online experiments, and the accuracy and generalizability were demonstrated.
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