Learning Task Constraints in Operational Space Formulation

Hsiu-Chin Lin, Prabhakar Ray, Matthew Howard

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Many human skills can be described in terms of performing a set of prioritised tasks. While a number of tools have become available that recover the underlying
control policy from constrained movements, few have explicitly considered learning how constraints should be imposed in order to perform the control policy. In this paper, a method for learning the self-imposed constraints present in movement observations is proposed. The problem is formulated into the
operational space control framework, where the goal is to estimate the constraint matrix and its null space projection that decompose the task space and any redundant degrees of freedom. The proposed method requires no prior knowledge about either the dimensionality of the constraints nor the
underlying control policies. The techniques are evaluated on a simulated three degree-of-freedom arm and on the AR10 humanoid hand.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Robotics and Automation (ICRA)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
ISBN (Electronic)978-1-5090-4633-1
Publication statusPublished - 24 Jul 2017
Event2017 IEEE International Conference on Robotics and Automation - Singapore, Singapore
Duration: 29 May 20173 Jun 2017


Conference2017 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2017
Internet address

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