Generalizable manipulation requires that robots be able to interact with novel objects and environment. This requirement makes manipulation extremely challenging as a robot has to reason about complex frictional interactions with uncertainty in physical properties of the object and the environment. In this article, we study robust optimization for planning of pivoting manipulation in the presence of uncertainties. We present insights about how friction can be exploited to compensate for inaccuracies in the estimates of the physical properties during manipulation. Under certain assumptions, we derive analytical expressions for stability margin provided by friction during pivoting manipulation. This margin is then used in a contact implicit bilevel optimization framework to optimize a trajectory that maximizes this stability margin to provide robustness against uncertainty in several physical parameters of the object. We present analysis of the stability margin with respect to several parameters involved in the underlying bilevel optimization problem. We demonstrate our proposed method using a 6 DoF manipulator for manipulating several different objects. We also design and validate an MPC controller using the proposed algorithm which can track and regulate the position of the object during manipulation.

中文翻译：

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使用接触隐式双层优化的鲁棒旋转操纵


可推广的操纵要求机器人能够与新的物体和环境进行交互。这一要求使得操作变得极具挑战性，因为机器人必须推理复杂的摩擦相互作用以及物体和环境物理特性的不确定性。在本文中，我们研究了在存在不确定性的情况下对旋转操纵进行规划的鲁棒优化。我们提出了关于如何利用摩擦来补偿操作过程中物理特性估计中的不准确性的见解。在某些假设下，我们推导了枢转操作期间摩擦力提供的稳定裕度的解析表达式。然后，在接触隐式双层优化框架中使用该裕度来优化最大化该稳定裕度的轨迹，从而提供针对对象的多个物理参数的不确定性的鲁棒性。我们对基础双层优化问题中涉及的几个参数的稳定性裕度进行了分析。我们展示了我们提出的方法，使用 6 DoF 操纵器来操纵几个不同的物体。我们还使用所提出的算法设计并验证了 MPC 控制器，该控制器可以在操纵过程中跟踪和调节物体的位置。