This report describes our approach for Phase 3 of the Real Robot Challenge. To solve cuboid manipulation tasks of varying difficulty, we decompose each task into the following primitives: moving the fingers to the cuboid to grasp it, turning it on the table to minimize orientation error, and re-positioning it to the goal position. We use model-based trajectory optimization and control to plan and execute these primitives. These grasping, turning, and re-positioning primitives are sequenced with a state-machine that determines which primitive to execute given the current object state and goal. Our method shows robust performance over multiple runs with randomized initial and goal positions. With this approach, our team placed second in the challenge, under the anonymous name "sombertortoise" on the leaderboard. Example runs of our method solving each of the four levels can be seen in this video (https://www.youtube.com/watch?v=I65Kwu9PGmg&list=PLt9QxrtaftrHGXcp4Oh8-s_OnQnBnLtei&index=1).

翻译：本报告描述我们对真实机器人挑战第3阶段的处理方法。 为了解决困难程度不同的幼崽操作任务, 我们将每个任务分解为以下原始任务: 将手指移到幼崽上以掌握它, 将手指移到幼崽上以尽量减少方向错误, 并将其重新定位到目标位置 。 我们使用模型的轨迹优化和控制来规划和执行这些原始。 这些抓取、 转转和重新定位原始的顺序是用州机器来决定根据当前对象状态和目标执行哪个原始任务。 我们的方法显示在多个运行中, 以随机初始位置和目标位置进行 。 通过这种方法, 我们的团队在挑战中排在第二位, 匿名名称为“ 检测” 。 我们解决四个级别的方法的示例运行可见于此视频 (https://www.youtube.com/ watch?v=I65Kwu9PG&list=PLt9QxtaftHGXp4O08- s_ s_ OnBnLtei&index=1)。