Amplify：用于机器人学中可复现非线性规划问题的轻量级库
Amplify: A Lightweight Library for Reproducible Nonlinear Programming Problems in Robotics
浏览论文内容
中文总结 AI 辅助
本文提出Amplify，一个轻量级非线性规划库，通过将轨迹优化算法表示为优化模型中的约束，实现机器人相关优化问题的可复现性，并与其他库进行了基准比较。
中文摘要 AI 辅助
优化问题（OPs）是解决机器人学中许多具有挑战性研究问题的关键。然而，可复现性仍然是一个主要问题。在本文中，我们提出了Amplify，一个轻量级非线性规划库，旨在实现机器人相关轨迹优化问题的可复现结果。这个537行（每行80个字符）的库的最低要求是互联网连接、熟悉AMPL建模语言以及一个文本编辑器。我们的主要贡献是构建了一个库，其中轨迹优化算法直接表示在优化模型中。具体来说，我们以声明式编程范式将用于计算动力学、轨迹和参考运动的算法实现为优化问题的约束。我们概述了我们的目标、决策变量和约束的公式如何能够被其他希望轻量且可复现的转录库所采用。我们还将Amplify框架与其他3个库在多个领域的基准优化问题示例上进行了比较，包括双足行走和抓取规划。
英文摘要
Optimization problems (OPs) are key to solving many challenging research problems in robotics. However, reproducibility still remains a major issue. In this paper, we present Amplify, a lightweight nonlinear programming library aimed at reproducible results of robotic-related trajectory optimization problems. The minimalistic requirements for the 537-line library (80 characters per line) are an Internet connection, familiarity with the AMPL modeling language, and a text editor. Our primary contribution is the formulation of a library where trajectory optimization algorithms are represented directly within the optimization model. Specifically, we implement the algorithms used to compute the dynamics, trajectories, and reference motions as constraints of the OP in a declarative programming paradigm. We outline how our formulation of objectives, decisions variables, and constraints can be implemented in other transcription libraries that want to be lightweight and reproducible. We also compare the Amplify framework with 3 other libraries across examples of benchmark optimization problems across several fields, including bipedal locomotion and grasp planning.
