基于松弛优化的机器人手内操作
Robotic in-hand manipulation with relaxed optimization
AI总结：
本文提出基于人类演示生成运动基元字典，并通过松弛优化组合基元生成指尖轨迹，使机器人手无需显式建模稳定性约束即可实现灵巧手内操作。
摘要：
灵巧的手内操作是一项独特而有价值的人类技能，需要在尊重稳定性约束的同时与环境进行复杂的感知运动交互。对于机器人平台而言，使生成的运动满足这些约束对于实现可靠的手内操作技能至关重要。显式地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演示被隐式地建模和学习。我们提出了一种基于从人类演示生成的运动基元字典的学习与控制方法。为实现这一目标，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处理物体，在不需要显式形式化的情况下遵守稳定性约束。换句话说，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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
