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.

本文提出了一个可微分端到端框架来优化接触感知机器人设计的两个关键部分：一种新颖的变形参数化方法和一个可处理联系丰富情况和计算全谱运动和动态参数分析梯度的可微刚体模拟器，在多个操作任务中，我们的框架胜过了使用其他表示形式优化控制或设计，或使用无梯度方法进行共同优化的现有方法的情况。