We explore how high-speed robot arm motions can dynamically manipulate cables to vault over obstacles, knock objects from pedestals, and weave between obstacles. In this paper, we propose a self-supervised learning framework that enables a UR5 robot to perform these three tasks. The framework finds a 3D apex point for the robot arm, which, together with a task-specific trajectory function, defines an arcing motion that dynamically manipulates the cable to perform tasks with varying obstacle and target locations. The trajectory function computes minimum-jerk motions that are constrained to remain within joint limits and to travel through the 3D apex point by repeatedly solving quadratic programs to find the shortest and fastest feasible motion. We experiment with 5 physical cables with different thickness and mass and compare performance against two baselines in which a human chooses the apex point. Results suggest that a baseline with a fixed apex across the three tasks achieves respective success rates of 51.7%, 36.7%, and 15.0%, and a baseline with human-specified, task-specific apex points achieves 66.7%, 56.7%, and 15.0% success rate respectively, while the robot using the learned apex point can achieve success rates of 81.7% in vaulting, 65.0% in knocking, and 60.0% in weaving. Code, data, and supplementary materials are available at https: //sites.google.com/berkeley.edu/dynrope/home.

翻译：我们探究了高速机器人臂如何通过动态运动操控线缆，实现跨越障碍物、击倒基座上的物体以及在障碍物间穿梭。本文提出一种自监督学习框架，使UR5机器人能够完成这三类任务。该框架通过为机器人臂寻找三维顶点，并结合任务特定的轨迹函数，定义出弧形运动轨迹，从而动态操控线缆以适应不同障碍物和目标位置。轨迹函数通过反复求解二次规划生成最小加加速度运动，确保运动始终满足关节限位约束，并确保通过三维顶点以实现最短且最快速的可行运动。我们使用5种不同粗细和质量的物理线缆进行实验，并与人选顶点两种基线进行性能对比。结果表明：在三项任务中使用固定顶点基线分别获得51.7%、36.7%和15.0%的成功率；人为指定任务特定顶点基线分别获得66.7%、56.7%和15.0%的成功率；而采用学习所得顶点的机器人在跨越、击倒和穿梭任务中分别达到81.7%、65.0%和60.0%的成功率。代码、数据及补充材料详见https://sites.google.com/berkeley.edu/dynrope/home。