This paper introduces the first, open source software library for Constraint Consistent Learning (CCL). It implements a family of data-driven methods that are capable of (i) learning state-independent and -dependent constraints, (ii) decomposing the behaviour of redundant systems into task- and null-space parts, and (iii) uncovering the underlying null space control policy. It is a tool to analyse and decompose many everyday tasks, such as wiping, reaching and drawing. The library also includes several tutorials that demonstrate its use with both simulated and real world data in a systematic way. This paper documents the methods contained within the library, including the implementations of said methods in tutorials and associated helper methods. The software is made freely available to the community, to enable code reuse and allow users to gain in-depth experience in statistical learning in this area. GRAPHICAL ABSTRACT

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用于约束一致学习的库

本文介绍了第一个用于约束一致学习 (CCL) 的开源软件库。它实现了一系列数据驱动的方法，能够（i）学习与状态无关和依赖的约束，（ii）将冗余系统的行为分解为任务空间和零空间部分，以及（iii）揭示底层零空间控制策略。它是一种分析和分解许多日常任务的工具，例如擦拭、到达和绘图。该库还包括几个教程，以系统的方式演示其在模拟和现实世界数据中的使用。本文记录了库中包含的方法，包括教程中所述方法的实现和相关的辅助方法。该软件免费提供给社区，实现代码重用，让用户在这方面的统计学习中获得深入的经验。图形概要