外观
论文arXiv 2609.283771 个月前
Amplify：面向机器人可复现非线性规划问题的轻量级库
原文链接：Amplify: A Lightweight Library for Reproducible Nonlinear Programming Problems in Robotics（arXiv 2609.28377 · 发布日期：2026-09-23）
Amplify: A Lightweight Library for Reproducible Nonlinear Programming Problems in Robotics
提出轻量级非线性规划库 Amplify，用于机器人轨迹优化可复现，并在多领域基准优化问题上比较验证。
核心要点
- 机器人优化问题应用广泛，但相关研究结果的可复现性仍是主要问题。
- Amplify 将轨迹优化算法直接表示在优化模型中，采用声明式约束实现。
- 该库仅 537 行，最低需求为联网、熟悉 AMPL 建模语言和文本编辑器。
- 在含双足运动和抓取规划的基准优化问题中，与另外 3 个库进行比较。
验证范围与局限：验证范围为多领域基准优化问题示例，包括双足运动和抓取规划。
英文摘要 (Abstract)
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.
作者：Rosa, Nelson, Jr
来源
本页事实对应的公开页面，以原文为准。