论文标题
控制级联
Controlling the Cascade: Kinematic Planning for N-ball Toss Juggling
论文作者
论文摘要
动态运动在人类运动行为中无处不在，因为它们倾向于更有效，并且可以比其准静态对应物解决更广泛的技能领域。几十年来，机器人杂耍任务一直是最常研究的动态操纵问题之一，因为所需的动态灵活性可以缩放到任意的难度高。但是，成功的方法仅限于基本的杂耍技能，这表明缺乏对灵巧折磨所需的限制。我们详细介绍了杂耍任务，确定关键挑战并将其形式化为轨迹优化问题。在我们最先进的，现实世界中的杂耍平台的基础上，我们达到了模拟中折磨的理论限制，评估了在不同难度的环境中产生的实时控制器，并在两个拟人型操纵器上最多可将17个球扔掉17个球。
Dynamic movements are ubiquitous in human motor behavior as they tend to be more efficient and can solve a broader range of skill domains than their quasi-static counterparts. For decades, robotic juggling tasks have been among the most frequently studied dynamic manipulation problems since the required dynamic dexterity can be scaled to arbitrarily high difficulty. However, successful approaches have been limited to basic juggling skills, indicating a lack of understanding of the required constraints for dexterous toss juggling. We present a detailed analysis of the toss juggling task, identifying the key challenges and formalizing it as a trajectory optimization problem. Building on our state-of-the-art, real-world toss juggling platform, we reach the theoretical limits of toss juggling in simulation, evaluate a resulting real-time controller in environments of varying difficulty and achieve robust toss juggling of up to 17 balls on two anthropomorphic manipulators.
