A Library for Constraint Consistent Learning
It provides a tool for researchers and practitioners to analyze and decompose everyday tasks, enabling code reuse and learning in statistical methods, but it is incremental as it focuses on software implementation rather than new algorithms.
The paper introduces the first open-source software library for Constraint Consistent Learning (CCL), which implements methods to learn constraints, decompose system behavior, and uncover control policies for tasks like wiping and reaching.
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.