# A Library for Constraint Consistent Learning

arXiv: https://arxiv.org/abs/1807.04676 · HTML: https://mlsanity.com/paper/1807.04676/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Constrained Equation Learner Networks for Precision-Preserving Extrapolation of Robotic Skills](https://mlsanity.com/paper/2311.02475/) (2023)

## Abstract (arXiv)

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 implementation of the library, 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.