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Robotic Manipulation under Uncertainty and Limited Dexterity
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0002-3653-4691
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: urn:nbn:se:kth:diva-187484ISBN: 978-91-7729-022-3 (print)OAI: oai:DiVA.org:kth-187484DiVA: diva2:930543
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
List of papers
1. An Adaptive Control Approach for Opening Doors and Drawers Under Uncertainties
Open this publication in new window or tab >>An Adaptive Control Approach for Opening Doors and Drawers Under Uncertainties
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2016 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 32, no 1, 161-175 p.Article in journal (Refereed) Published
Abstract [en]

We study the problem of robot interaction with mechanisms that afford one degree of freedom motion, e.g., doors and drawers. We propose a methodology for simultaneous compliant interaction and estimation of constraints imposed by the joint. Our method requires no prior knowledge of the mechanisms' kinematics, including the type of joint, prismatic or revolute. The method consists of a velocity controller that relies on force/torque measurements and estimation of the motion direction, the distance, and the orientation of the rotational axis. It is suitable for velocity controlled manipulators with force/torque sensor capabilities at the end-effector. Forces and torques are regulated within given constraints, while the velocity controller ensures that the end-effector of the robot moves with a task-related desired velocity. We give proof that the estimates converge to the true values under valid assumptions on the grasp, and error bounds for setups with inaccuracies in control, measurements, or modeling. The method is evaluated in different scenarios involving opening a representative set of door and drawer mechanisms found in household environments.

Place, publisher, year, edition, pages
IEEE, 2016
Keyword
Adaptive control, calibration and identification, force/motion control, service robots, uncertain kinematics
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-184046 (URN)10.1109/TRO.2015.2506154 (DOI)000370764000012 ()2-s2.0-84961994390 (Scopus ID)
Note

QC 20160323

Available from: 2016-03-23 Created: 2016-03-22 Last updated: 2017-11-30Bibliographically approved
2. Online Contact Point Estimation for Uncalibrated Tool Use
Open this publication in new window or tab >>Online Contact Point Estimation for Uncalibrated Tool Use
2014 (English)In: Robotics and Automation (ICRA), 2014 IEEE International Conference on, IEEE Robotics and Automation Society, 2014, 2488-2493 p.Conference paper, Published paper (Refereed)
Abstract [en]

One of the big challenges for robots working outside of traditional industrial settings is the ability to robustly and flexibly grasp and manipulate tools for various tasks. When a tool is interacting with another object during task execution, several problems arise: a tool can be partially or completely occluded from the robot's view, it can slip or shift in the robot's hand - thus, the robot may lose the information about the exact position of the tool in the hand. Thus, there is a need for online calibration and/or recalibration of the tool. In this paper, we present a model-free online tool-tip calibration method that uses force/torque measurements and an adaptive estimation scheme to estimate the point of contact between a tool and the environment. An adaptive force control component guarantees that interaction forces are limited even before the contact point estimate has converged. We also show how to simultaneously estimate the location and normal direction of the surface being touched by the tool-tip as the contact point is estimated. The stability of the the overall scheme and the convergence of the estimated parameters are theoretically proven and the performance is evaluated in experiments on a real robot.

Place, publisher, year, edition, pages
IEEE Robotics and Automation Society, 2014
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-165631 (URN)10.1109/ICRA.2014.6907206 (DOI)
Conference
IEEE International Conference on Robots and Automation,Hong Kong, May 31 2014-June 7 2014
Note

QC 20150507

Available from: 2015-04-29 Created: 2015-04-29 Last updated: 2016-05-24Bibliographically approved
3. Predicting Slippage and Learning Manipulation Affordances through Gaussian Process Regression
Open this publication in new window or tab >>Predicting Slippage and Learning Manipulation Affordances through Gaussian Process Regression
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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
robotic grasping, robotic manipulation
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-134125 (URN)10.1109/HUMANOIDS.2013.7030015 (DOI)2-s2.0-84937838836 (Scopus ID)
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
4. In-hand manipulation using gravity and controlled slip
Open this publication in new window or tab >>In-hand manipulation using gravity and controlled slip
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2015 (English)In: Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, IEEE conference proceedings, 2015, 5636-5641 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this work we propose a sliding mode controllerfor in-hand manipulation that repositions a tool in the robot’shand by using gravity and controlling the slippage of the tool. In our approach, the robot holds the tool with a pinch graspand we model the system as a link attached to the grippervia a passive revolute joint with friction, i.e., the grasp onlyaffords rotational motions of the tool around a given axis ofrotation. The robot controls the slippage by varying the openingbetween the fingers in order to allow the tool to move tothe desired angular position following a reference trajectory.We show experimentally how the proposed controller achievesconvergence to the desired tool orientation under variations ofthe tool’s inertial parameters.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015
Keyword
robotics, manipulation, in-hand manipulation, extrinsic dexterity, friction, slip
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-178958 (URN)10.1109/IROS.2015.7354177 (DOI)000371885405113 ()2-s2.0-84958153950 (Scopus ID)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems
Funder
EU, FP7, Seventh Framework Programme, ICT-288533
Note

QC 20160112

Available from: 2015-12-09 Created: 2015-12-09 Last updated: 2016-05-24Bibliographically approved
5. Adaptive Control for Pivoting with Visual and Tactile Feedback
Open this publication in new window or tab >>Adaptive Control for Pivoting with Visual and Tactile Feedback
2016 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this work we present an adaptive control approach for pivoting, which is an in-hand manipulation maneuver that consists of rotating a grasped object to a desired orientation relative to the robot’s hand. We perform pivoting by means of gravity, allowing the object to rotate between the fingers of a one degree of freedom gripper and controlling the gripping force to ensure that the object follows a reference trajectory and arrives at the desired angular position. We use a visual pose estimation system to track the pose of the object and force measurements from tactile sensors to control the gripping force. The adaptive controller employs an update law that accommodates for errors in the friction coefficient,which is one of the most common sources of uncertainty in manipulation. Our experiments confirm that the proposed adaptive controller successfully pivots a grasped object in the presence of uncertainty in the object’s friction parameters.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-187483 (URN)000389516200050 ()2-s2.0-84977472497 (Scopus ID)
Conference
IEEE International Conference on Robotics and Automation,Stockholm, Sweden 16-21 May 2016
Note

QC 20160524

Available from: 2016-05-24 Created: 2016-05-24 Last updated: 2017-01-19Bibliographically approved

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Viña Barrientos, Francisco

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