Title
Learning of Planning Models for Dexterous Manipulation Based on Human Demonstrations.
Abstract
In the human environment service robots have to be able to manipulate autonomously a large variety of objects in a workspace restricted by collisions with obstacles, self-collisions and task constraints. Planning enables the robot system to generalize predefined or learned manipulation knowledge to new environments. For dexterous manipulation tasks the manual definition of planning models is time-consuming and error-prone. In this work, planning models for dexterous tasks are learned based on multiple human demonstrations using a general feature space including automatically generated contact constraints, which are automatically relaxed to consider the correspondence problem. In order to execute the learned planning model with different objects, the contact location is transformed to given object geometry using morphing. The initial, overspecialized planning model is generalized using a previously described, parallelized optimization algorithm with the goal to find a maximal subset of task constraints, which admits a solution to a set of test problems. Experiments on two different, dexterous tasks show the applicability of the learning approach to dexterous manipulation tasks.
Year
DOI
Venue
2012
10.1007/s12369-012-0162-y
I. J. Social Robotics
Keywords
Field
DocType
Programming by demonstration,Motion planning,Machine learning
Motion planning,Programming by demonstration,Morphing,Feature vector,Computer science,Workspace,Simulation,Artificial intelligence,Correspondence problem,Robot,Dexterous manipulation
Journal
Volume
Issue
ISSN
4
4
1875-4791
Citations 
PageRank 
References 
6
0.43
22
Authors
6
Name
Order
Citations
PageRank
Rainer Jäkel1635.99
Sven R. Schmidt-Rohr21038.80
Steffen W. Rühl380.85
Alexander Kasper4904.67
Zhixing Xue515712.45
Rüdiger Dillmann62201262.95