Robotic grippers are receiving increasing attention in various industries as essential components of robots for interacting and manipulating objects. While significant progress has been made in the past, conventional rigid grippers still have limitations in handling irregular objects and can damage fragile objects. We have shown that soft grippers offer deformability to adapt to a variety of object shapes and maximize object protection. At the same time, dynamic vision sensors (e.g., event-based cameras) are capable of capturing small changes in brightness and streaming them asynchronously as events, unlike RGB cameras, which do not perform well in low-light and fast-moving environments. In this paper, a dynamic-vision-based algorithm is proposed to measure the force applied to the gripper. In particular, we first set up a DVXplorer Lite series event camera to capture twenty-five sets of event data. Second, motivated by the impressive performance of the Vision Transformer (ViT) algorithm in dense image prediction tasks, we propose a new approach that demonstrates the potential for real-time force estimation and meets the requirements of real-world scenarios. We extensively evaluate the proposed algorithm on a wide range of scenarios and settings, and show that it consistently outperforms recent approaches.

翻译：机器人夹爪作为机器人交互与操作物体的核心部件，正日益受到各行各业的关注。尽管过去已取得显著进展，传统刚性夹爪在处理不规则物体时仍存在局限，且可能损坏易碎物品。研究表明，软体夹爪具备可变形性，能够适应多种物体形状并最大限度保护物体。与此同时，动态视觉传感器（如事件相机）能够捕捉亮度的微小变化，并以异步事件流的形式输出，这与在低光照和快速运动环境中表现不佳的RGB相机形成对比。本文提出一种基于动态视觉的算法，用于测量施加于夹爪上的力。具体而言，我们首先搭建DVXplorer Lite系列事件相机，采集了二十五组事件数据。其次，受视觉Transformer（ViT）算法在密集图像预测任务中卓越表现的启发，我们提出一种新方法，展示了其在实时力估计中的潜力，并满足实际应用场景的需求。我们通过大量场景与设置下的全面评估表明，该算法始终优于近期提出的方法。