目前机器人操作的主导范式涉及两个独立的阶段:机械手设计和控制。由于机器人的形态和控制方式紧密相连，关节的优化设计和控制可以显著提高性能。现有的协同优化方法是有限的，未能探索出丰富的设计空间。主要原因是设计的复杂性与制造、优化、接触处理等实际约束之间的权衡。我们通过为接触式机器人设计构建端到端可微分的框架，克服了其中的几个挑战。这个框架的两个关键组件是: 一种新颖的基于变形的参数化，允许设计具有任意、复杂几何结构的铰接刚性机器人，以及一个可微分刚体模拟器，可以处理丰富的接触场景，并为运动学和动力学参数的全谱计算解析梯度。在多个操作任务中，我们的框架优于现有的方法，这些方法要么只优化控制，要么使用替代表示进行设计，要么使用无梯度方法进行协同优化。
原文题目：An End-to-End Differentiable Framework for Contact-Aware Robot Design
原文：The current dominant paradigm for robotic manipulation involves two separate stages: manipulator design and control. Because the robot’s morphology and how it can be controlled are intimately linked, joint optimization of design and control can significantly improve performance. Existing methods for co-optimization are limited and fail to explore a rich space of designs. The primary reason is the trade-off between the complexity of designs that is necessary for contact-rich tasks against the practical constraints of manufacturing, optimization, contact handling, etc. We overcome several of these challenges by building an end-to-end differentiable framework for contactaware robot design. The two key components of this framework are: a novel deformation-based parameterization that allows for the design of articulated rigid robots with arbitrary, complex geometry, and a differentiable rigid body simulator that can handle contact-rich scenarios and computes analytical gradients for a full spectrum of kinematic and dynamic parameters. On multiple manipulation tasks, our framework outperforms existing methods that either only optimize for control or for design using alternate representations or co-optimize using gradient-free methods.
[An End-to-End Differentiable Framework for Contact-Aware Robot Design.pdf]