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 contact-aware 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.

中文翻译：

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一种用于接触感知机器人设计的端到端可微框架

当前机器人操纵的主导范式涉及两个独立的阶段：机械手设计和控制。由于机器人的形态及其控制方式密切相关，设计和控制的联合优化可以显着提高性能。现有的协同优化方法是有限的，无法探索丰富的设计空间。主要原因是接触丰富的任务所需的设计复杂性与制造、优化、接触处理等的实际约束之间的权衡。我们通过构建端到端可微分来克服其中的几个挑战接触感知机器人设计框架。该框架的两个关键组成部分是：一种新颖的基于变形的参数化，允许设计具有任意、复杂的几何形状，以及可微分刚体模拟器，可以处理接触丰富的场景并计算全谱运动学和动力学参数的分析梯度。在多个操作任务上，我们的框架优于现有的方法，这些方法要么仅针对控制进行优化，要么针对使用替代表示的设计进行优化，或者使用无梯度方法进行协同优化。