A Library for Constraint Consistent Learning
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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 powerful tool to analyse and decompose many everyday tasks, such as wiping, reaching and drawing. The library also includes several tutorials that demonstrate its use 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 indepth experience in learning constraints and underlying control policies in the context of redundant robotic systems.
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