Learning Task Constraints in Operational Space Formulation

Hsiu-Chin Lin • Prabhakar Ray • Matthew Howard

09:55 - 10:00 | Tuesday 30 May 2017 | Room 4611/4612



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 the constraints should be imposed in order to perform the tasks. 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 ARlO humanoid hand.