Bimanual Rope Manipulation Skill Synthesis through Context Dependent Correction Policy Learning from Human Demonstration
Bimanual Rope Manipulation Skill Synthesis through Context Dependent Correction Policy Learning from Human Demonstration
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DOI：
10.1109/icra48891.2023.10160895
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
T. Akbulut;G. Girgin;A. Mehrabi;M. Asada;Emre Ugur;E. Oztop
中科院分区：
文献类型：
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作者：
T. Akbulut;G. Girgin;A. Mehrabi;M. Asada;Emre Ugur;E. Oztop
通过行为克隆从演示中学习（LfD）因其简单性而具有吸引力;然而，在长时间和复杂的技能中复合错误可能是一个障碍。在这方面，将目标技能视为运动基元序列是有帮助的。然后，必须满足电机原语在允许后续原语成功执行的状态下结束的要求。在这项研究中，我们专注于这个问题，提出学习一个明确的纠正政策时，没有达到预期的过渡状态之间的原语。校正策略是通过使用条件神经运动原语（CNMP）的行为克隆来学习的，CNMP可以以上下文相关的方式生成校正轨迹。所提出的系统的优势，学习完整的任务作为一个单一的行动显示与桌面设置在模拟，其中一个对象必须通过走廊推在两个步骤。然后，所提出的方法的适用性，以双手动打结在真实的世界是通过装备一个上身人形机器人的技能，使结在一个酒吧在3D空间。
Learning from demonstration (LfD) with behavior cloning is attractive for its simplicity; however, compounding errors in long and complex skills can be a hindrance. Considering a target skill as a sequence of motor primitives is helpful in this respect. Then the requirement that a motor primitive ends in a state that allows the successful execution of the subsequent primitive must be met. In this study, we focus on this problem by proposing to learn an explicit correction policy when the expected transition state between primitives is not achieved. The correction policy is learned via behavior cloning by the use of Conditional Neural Motor Primitives (CNMPs) that can generate correction trajectories in a context-dependent way. The advantage of the proposed system over learning the complete task as a single action is shown with a table-top setup in simulation, where an object has to be pushed through a corridor in two steps. Then, the applicability of the proposed method to bi-manual knotting in the real world is shown by equipping an upper-body humanoid robot with the skill of making knots over a bar in 3D space.