Learning Null Space Projections Fast

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

2 Citations (Scopus)
56 Downloads (Pure)


Typically robot interactions with the environment may involve some type of constraint which impedes the motion of the system. This paper proposes an approach to learn kinematic constraints from observed movements. Our method derives the null space projection of a kinematically constrained system using gradient descent. Moreover, we compare this method to the existing brute force-based approach for learning constraints on data sets of different dimensionality, to demonstrate how it can learn constraints from data sets of a much higher dimensionality.
Original languageEnglish
Title of host publication European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Number of pages6
Publication statusPublished - 2017


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