目前機器人操作的主導範式涉及兩個獨立的階段:機械手設計和控制。由于機器人的形态和控制方式緊密相連，關節的優化設計和控制可以顯著提高性能。現有的協同優化方法是有限的，未能探索出豐富的設計空間。主要原因是設計的複雜性與制造、優化、接觸處理等實際限制之間的權衡。我們通過為接觸式機器人設計建構端到端可微分的架構，克服了其中的幾個挑戰。這個架構的兩個關鍵元件是: 一種新穎的基于變形的參數化，允許設計具有任意、複雜幾何結構的鉸接剛性機器人，以及一個可微分剛體模拟器，可以處理豐富的接觸場景，并為運動學和動力學參數的全譜計算解析梯度。在多個操作任務中，我們的架構優于現有的方法，這些方法要麼隻優化控制，要麼使用替代表示進行設計，要麼使用無梯度方法進行協同優化。
原文題目：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]