Title
Learning Competing Constraints and Task Priorities from Demonstrations of Bimanual Skills.
Abstract
As bimanual robots become increasingly popular, learning and control algorithms must take into account new constraints and challenges imposed by this morphology. Most research on learning bimanual skills has focused on learning coordination between end-effectors, exploiting operational space formulations. However, motion patterns in bimanual scenarios are not exclusive to operational space, also occurring at the joint level. Moreover, bimanual operation offers the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization in bimanual settings. Here we address the aforementioned problems from a robot learning perspective. We go beyond operational space and present a principled approach to simultaneously learn operational and configuration space constraints, as well as the evolution of task priorities from demonstrations. Our method extends the Task-Parameterized Gaussian Mixture Model (TP-GMM) to the use of projection operators which allow for tackling such problems. The approach is validated in two different bimanual tasks with the COMAN and WALK-MAN humanoids that either require the consideration of constraints in both operational and configuration spaces, or the prioritization of tasks.
Year
Venue
Field
2017
arXiv: Robotics
Robot learning,Control algorithm,Computer science,Prioritization,Operator (computer programming),Artificial intelligence,Robot,Mixture model,Configuration space
DocType
Volume
Citations 
Journal
abs/1707.06791
0
PageRank 
References 
Authors
0.34
21
4
Name
Order
Citations
PageRank
João Silvério101.01
Sylvain Calinon21897117.63
Leonel Rozo315813.95
Darwin G. Caldwell42900319.72