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Predicting Slippage and Learning Manipulation Affordances through Gaussian Process Regression
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-3653-4691
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. (CAS)ORCID iD: 0000-0003-2078-8854
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0001-5129-342X
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2013 (English)In: Proceeding of the 2013 IEEE-RAS International Conference on Humanoid Robots, IEEE Computer Society, 2013Conference paper, Published paper (Refereed)
Abstract [en]

Object grasping is commonly followed by someform of object manipulation – either when using the grasped object as a tool or actively changing its position in the hand through in-hand manipulation to afford further interaction. In this process, slippage may occur due to inappropriate contact forces, various types of noise and/or due to the unexpected interaction or collision with the environment. In this paper, we study the problem of identifying continuous bounds on the forces and torques that can be applied on a grasped object before slippage occurs. We model the problem as kinesthetic rather than cutaneous learning given that the measurements originate from a wrist mounted force-torque sensor. Given the continuous output, this regression problem is solved using a Gaussian Process approach.We demonstrate a dual armed humanoid robot that can autonomously learn force and torque bounds and use these to execute actions on objects such as sliding and pushing. We show that the model can be used not only for the detection of maximum allowable forces and torques but also for potentially identifying what types of tasks, denoted as manipulation affordances, a specific grasp configuration allows. The latter can then be used to either avoid specific motions or as a simple step of achieving in-hand manipulation of objects through interaction with the environment.

Place, publisher, year, edition, pages
IEEE Computer Society, 2013.
Keyword [en]
robotic grasping, robotic manipulation
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-134125DOI: 10.1109/HUMANOIDS.2013.7030015Scopus ID: 2-s2.0-84937838836OAI: oai:DiVA.org:kth-134125DiVA: diva2:664977
Conference
2013 IEEE-RAS International Conference on Humanoid Robots, October 15 - October 17, 2013
Funder
EU, FP7, Seventh Framework Programme, FP7-ICT-288533Swedish Research CouncilSwedish Foundation for Strategic Research
Note

QC 20130930

Available from: 2013-11-18 Created: 2013-11-18 Last updated: 2016-05-24Bibliographically approved
In thesis
1. Robotic Manipulation under Uncertainty and Limited Dexterity
Open this publication in new window or tab >>Robotic Manipulation under Uncertainty and Limited Dexterity
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Robotic manipulators today are mostly constrained to perform fixed, repetitive tasks. Engineers design the robot’s workcell specifically tailoredto the task, minimizing all possible uncertainties such as the location of tools and parts that the robot manipulates. However, autonomous robots must be capable of manipulating novel objects with unknown physical properties such as their inertial parameters, friction and shape. In this thesis we address the problem of uncertainty connected to kinematic constraints and friction forces in several robotic manipulation tasks. We design adaptive controllers for opening one degree of freedom mechanisms, such as doors and drawers, under the presence of uncertainty in the kinematic parameters of the system. Furthermore, we formulate adaptive estimators for determining the location of the contact point between a tool grasped by the robot and the environment in manipulation tasks where the robot needs to exert forces with the tool on another object, as in the case of screwing or drilling. We also propose a learning framework based on Gaussian Process regression and dual arm manipulation to estimate the static friction properties of objects. The second problem we address in this thesis is related to the mechanical simplicity of most robotic grippers available in the market. Their lower cost and higher robustness compared to more mechanically advanced hands make them attractive for industrial and research robots. However, the simple mechanical design restrictsthem from performing in-hand manipulation, i.e. repositioning of objects in the robot’s hand, by using the fingers to push, slide and roll the object. Researchers have proposed thus to use extrinsic dexterity instead, i.e. to exploit resources and features of the environment, such as gravity or inertial forces,  that can help the robot to perform regrasps. Given that the robot must then interact with the environment, the problem of uncertainty becomes highly relevant. We propose controllers for performing pivoting, i.e. reorienting the grasped object in the robot’s hand, using gravity and controlling the friction exerted by the fingertips by varying the grasping force.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. 43 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2016:15
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-187484 (URN)978-91-7729-022-3 (ISBN)
Public defence
2016-06-13, F3, Lindstedtsvägen 26, KTH Campus Valhallavägen, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20160524

Available from: 2016-05-24 Created: 2016-05-24 Last updated: 2016-05-30Bibliographically approved

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humanoids13_vbskk.pdf(3759 kB)93 downloads
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Viña, FranciscoSmith, ChristianKarayiannidis, YiannisKragic, Danica

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