Manipulating cables is challenging for robots because of the infinite degrees of freedom of the cables and frequent occlusion by the gripper and the environment. These challenges are further complicated by the dexterous nature of the operations required for cable routing and assembly, such as weaving and inserting, hampering common solutions with vision-only sensing. In this paper, we propose to integrate tactile-guided low-level motion control with high-level vision-based task parsing for a challenging task: cable routing and assembly on a reconfigurable task board. Specifically, we build a library of tactile-guided motion primitives using a fingertip GelSight sensor, where each primitive reliably accomplishes an operation such as cable following and weaving. The overall task is inferred via visual perception given a goal configuration image, and then used to generate the primitive sequence. Experiments demonstrate the effectiveness of individual tactile-guided primitives and the integrated end-to-end solution, significantly outperforming the method without tactile sensing. Our reconfigurable task setup and proposed baselines provide a benchmark for future research in cable manipulation. More details and video are presented in \url{https://helennn.github.io/cable-manip/}

翻译：线缆的无限自由度以及夹爪和环境的频繁遮挡使得机器人操作线缆极具挑战性。而线缆布线和装配所需的灵巧操作（如编织和插入）进一步加剧了这些难题，阻碍了仅依赖视觉感知的常规解决方案。本文针对可重构任务板上的线缆布线与装配这一挑战性任务，提出将触觉引导的低层运动控制与基于视觉的高层任务解析相结合。具体而言，我们利用指尖式GelSight传感器构建了触觉引导的运动基元库，每个基元可稳定完成诸如线缆跟随和编织等操作。通过给定目标配置图像的视觉感知推断整体任务，进而生成基元序列。实验表明，单个触觉引导基元与集成端到端解决方案的有效性，其性能显著优于无触觉感知的方法。我们提出的可重构任务装置与基线方法为未来线缆操作研究提供了基准。更多细节与视频请见https://helennn.github.io/cable-manip/。