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 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.
Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalAdvanced Robotics
Early online date7 Jul 2020
Publication statusE-pub ahead of print - 7 Jul 2020


  • Software Library
  • Constraints
  • Learning from Demonstration
  • Model Learning
  • learning from demonstration
  • Software library
  • constraints
  • model learning


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