Dexterous in-hand manipulation is a unique and valuable human skill requiring sophisticated sensorimotor interaction with the environment while respecting stability constraints. Satisfying these constraints with generated motions is essential for a robotic platform to achieve reliable in-hand manipulation skills. Explicitly modelling these constraints can be challenging, but they can be implicitly modelled and learned through experience or human demonstrations. We propose a learning and control approach based on dictionaries of motion primitives generated from human demonstrations. To achieve this, we defined an optimization process that combines motion primitives to generate robot fingertip trajectories for moving an object from an initial to a desired final pose. Based on our experiments, our approach allows a robotic hand to handle objects like humans, adhering to stability constraints without requiring explicit formalization. In other words, the proposed motion primitive dictionaries learn and implicitly embed the constraints crucial to the in-hand manipulation task.

翻译：灵巧的手内操作是人类独特且宝贵的技能，它需要与环境进行复杂的感知运动交互，同时满足稳定性约束。通过生成的运动来满足这些约束，对于机器人平台实现可靠的手内操作技能至关重要。显式建模这些约束可能具有挑战性，但它们可以通过经验或人类示范被隐式建模和学习。我们提出了一种基于从人类示范生成的运动基元字典的学习与控制方法。为此，我们定义了一个优化过程，该过程组合运动基元以生成机器人指尖轨迹，从而将物体从初始位姿移动到期望的最终位姿。根据我们的实验，我们的方法使机器人手能够像人类一样操作物体，遵守稳定性约束而无需显式形式化。换言之，所提出的运动基元字典学习并隐式嵌入了对手内操作任务至关重要的约束。