Draft：用于机器人设计探索的参数化工具

中文摘要 AI 辅助

机器人性能常常受到形态与控制协同迭代成本的限制，因为每次计算机辅助设计（CAD）的修改都必须在控制工作开始前转化为可仿真的模型。协同设计方法试图弥合这一差距，但每种方法都使用为单一平台编写的模型生成器，或者缺乏使用真实世界数据来表明设计是合理的。我们提出了Draft，一种参数化生成工具，其通用引擎将任何串行链的参数化树编译成无需CAD的仿真就绪MJCF模型。它允许工程师通过易于调整的模型探索设计权衡，并评估更改如何影响控制器性能。Draft利用对114个执行器和49个已发布机器人描述进行调查后拟合的趋势来锚定每个设计的自由参数，从而使生成的机器人锚定到真实世界的硬件。我们通过构建四个现成机器人的孪生模型来全面验证这些趋势，其质量在几何平均倍数误差上一致到1.10倍。最后，我们通过评估三个四足机器人经过两阶段强化学习课程，展示了Draft如何揭示设计权衡。

英文摘要

Robot performance is often limited by the cost of iterating on morphology and control together, since every computer-aided design (CAD) change has to be carried into a simulation-ready model before control work begins. Co-design methods attempt to close this gap, but each uses a model generator written for a single platform or lack the use of real-world data to suggest that designs are plausible. We present Draft, a parametric generation tool whose generalized engine compiles any parametric tree of serial chains into a simulation-ready MJCF model, without CAD. It allows engineers to explore design tradeoffs through easily adjustable models and evaluate how changes influence controller performance. Draft grounds the free parameters of each design using trends fitted to a survey of $114$ actuators and $49$ published robot descriptions, so that a generated robot is anchored to real-world hardware. We validate those trends wholistically by building twins of four off-the-shelf robots, whose masses agree to $1.10\times$ geometric mean fold error. Finally, we demonstrate how Draft exposes design tradeoffs by evaluating three quadrupeds through a two-stage reinforcement learning curriculum.

发表机构

• Massachusetts Institute of Technology（麻省理工学院）

机构由 AI 辅助整理，请以论文原文为准。