dc.contributor.author |
Liarokapis, Minas |
en |
dc.contributor.author |
Bechlioulis, CP |
en |
dc.contributor.author |
Boutselis, GI |
en |
dc.contributor.author |
Kyriakopoulos, KJ |
en |
dc.contributor.editor |
Bianchi, M |
en |
dc.contributor.editor |
Moscatelli, A |
en |
dc.date.accessioned |
2018-10-09T23:02:34Z |
en |
dc.date.issued |
2016 |
en |
dc.identifier.isbn |
978-3-319-26706-7 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/40136 |
en |
dc.description.abstract |
This chapter presents a learn by demonstration approach, for closed-loop, robust, anthropomorphic grasp planning. In this respect, human demonstrations are used to perform skill transfer between the human and the robot artifacts, mapping human to robot motion with functional anthropomorphism [1]. In this work we extend the synergistic description adopted in Chaps. 2–6 for human grasping, in Chap. 8 for robotic hand design and, finally, in Chap. 15 for hand pose reconstruction systems, to define a low-dimensional manifold where the extracted anthropomorphic robot arm hand system kinematics are projected and appropriate Navigation Function (NF) models are trained. The training of the NF models is performed in a task-specific manner, for various: (1) subspaces, (2) objects and (3) tasks to be executed with the corresponding object. A vision system based on RGB-D cameras (Kinect, Microsoft) provides online feedback, performing object detection, object pose estimation and triggering the appropriate NF models. The NF models formulate a closed-loop velocity control scheme, that ensures humanlikeness of robot motion and guarantees convergence to the desired goals. The aforementioned scheme is also supplemented with a grasping control methodology, that derives task-specific, force closure grasps, utilizing tactile sensing. This methodology takes into consideration the mechanical and geometric limitations imposed by the robot hand design and enables stable grasps of a plethora of everyday life objects, under a wide range of uncertainties. The efficiency of the proposed methods is verified through extensive experimental paradigms, with the Mitsubishi PA10 – DLR/HIT II 22 DoF robot arm hand system. |
en |
dc.publisher |
Springer International Publishing |
en |
dc.relation.ispartof |
Human and robot hands: Sensorimotor synergies to bridge the gap between neuroscience and robotics |
en |
dc.relation.ispartofseries |
Springer series on touch and haptic systems |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.title |
A learn by demonstration approach for closed-Loop, robust, anthropomorphic grasp planning |
en |
dc.type |
Book Item |
en |
dc.identifier.doi |
10.1007/978-3-319-26706-7_9 |
en |
pubs.begin-page |
127 |
en |
dc.rights.holder |
Copyright: The author |
en |
pubs.end-page |
149 |
en |
pubs.place-of-publication |
Switzerland |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.elements-id |
606886 |
en |
pubs.org-id |
Engineering |
en |
pubs.org-id |
Mechanical Engineering |
en |
pubs.number |
Part II |
en |
pubs.record-created-at-source-date |
2017-01-17 |
en |