Grasp planning under uncertainty

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Kyrki, Ville, Prof., Aalto University, Department of Automation and Systems Technology, Finland
dc.contributor.author Kolycheva, Ekaterina (née Nikandrova)
dc.date.accessioned 2016-01-05T10:01:47Z
dc.date.available 2016-01-05T10:01:47Z
dc.date.issued 2016
dc.identifier.isbn 978-952-60-6620-2 (electronic)
dc.identifier.isbn 978-952-60-6619-6 (printed)
dc.identifier.issn 1799-4942 (electronic)
dc.identifier.issn 1799-4934 (printed)
dc.identifier.issn 1799-4934 (ISSN-L)
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/19349
dc.description.abstract Advanced robots such as mobile manipulators offer nowadays great opportunities for realistic manipulators. Physical interaction with the environment is an essential capability for service robots when acting in unstructured environments such as homes. Thus, manipulation and grasping under uncertainty has become a critical research area within robotics research. This thesis explores techniques for a robot to plan grasps in presence of uncertainty in knowledge about objects such as their pose and shape. First, the question how much information about the graspable object the robot can perceive from a single tactile exploration attempt is considered. Next, a tactile-based probabilistic approach for grasping which aims to maximize the probability of a successful grasp is presented. The approach is further extended to include information gathering actions based on maximal entropy reduction. The combined framework unifies ideas behind planning for maximally stable grasps, the possibilities of sensor-based grasping and exploration. Another line of research is focused on grasping familiar object belonging to a specific category. Moreover, the task is also included in the planning process as in many applications the resulting grasp should be not only stable but task compatible. The vision-based framework takes the idea of maximizing grasp stability in the novel context to cover shape uncertainty. Finally, the RGB-D vision-based probabilistic approach is extended to include tactile sensor feedback in the control loop to incrementally improve estimates about object shape and pose and then generate more stable task compatible grasps. The results of the studies demonstrate the benefits of applying probabilistic models and using different sensor measurements in grasp planning and prove that this is a promising direction of study and research. Development of such approaches, first of all, contributes to the rapidly developing area of household applications and service robotics. en
dc.format.extent 77 + app. 90
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartofseries Aalto University publication series DOCTORAL DISSERTATIONS en
dc.relation.ispartofseries 9/2016
dc.relation.haspart [Publication 1]: E. Nikandrova and V. Kyrki. What do contacts tell about an object?. In Proceedings of the 2012 4th IEEE RAS and EMBS, International Conference on Biomedical Robotics and Biomechatronics, pages 1895-1900, Roma, Italy, June 2012. DOI: 10.1109/BioRob.2012.6290299
dc.relation.haspart [Publication 2]: J. Laaksonen, E. Nikandrova and V. Kyrki. Probabilistic Sensor-based Grasping. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2019-2026, Vilamoura, Algarve, Portugal, October 2012. DOI: 10.1109/IROS.2012.6385621
dc.relation.haspart [Publication 3]: E. Nikandrova and V. Kyrki. Explorative sensor-based grasp planning. In Towards Autonomous Robotic Systems (TAROS), pages 197-208, Bristol, UK, August 2012. DOI: 10.1007/978-3-642-32527-4_18
dc.relation.haspart [Publication 4]: E. Nikandrova, J. Laaksonen and V. Kyrki. Towards informative sensorbased grasp planning. Robotics and Autonomous Systems, Volume 62, Issue 3, pages 340-354, March 2014. DOI: 10.1016/j.robot.2013.09.009
dc.relation.haspart [Publication 5]: E. Nikandrova and V. Kyrki. Category-based task specific grasping. Robotics and Autonomous Systems, Volume 70, pages 25-35, August 2015. DOI: 10.1016/j.robot.2015.04.002
dc.relation.haspart [Publication 6]: E. Kolycheva and V. Kyrki. Task-specific Grasping of Similar Objects by Probabilistic Fusion of Vision and Tactile Measurements. Accepted for publication in IEEE-RAS International Conference on Humanoid Robots (Humanoids 2015), November 2015.
dc.subject.other Automation en
dc.title Grasp planning under uncertainty en
dc.type G5 Artikkeliväitöskirja fi
dc.contributor.school Sähkötekniikan korkeakoulu fi
dc.contributor.school School of Electrical Engineering en
dc.contributor.department Sähkötekniikan ja automaation laitos fi
dc.contributor.department Department of Electrical Engineering and Automation en
dc.subject.keyword grasp planning en
dc.subject.keyword probabilistic models en
dc.subject.keyword MCMC en
dc.subject.keyword GPR en
dc.subject.keyword entropy en
dc.subject.keyword PSO en
dc.subject.keyword optimization en
dc.identifier.urn URN:ISBN:978-952-60-6620-2
dc.type.dcmitype text en
dc.type.ontasot Doctoral dissertation (article-based) en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.contributor.supervisor Kyrki, Ville, Prof., Aalto University, Department of Automation and Systems Technology, Finland
dc.opn Ek, Carl, Prof., University of Bristol, UK
dc.contributor.lab Intelligent Robotics group en
dc.rev Detry, Renaud, Dr., Montefiore Institute University of Liege, Belgium
dc.rev Ciocarlie, Matei, Prof., Columbia University, USA
dc.date.defence 2016-01-22


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