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Pokorny, F. T., Bekiroglu, Y., Pauwels, K., Butepage, J., Scherer, C. & Kragic, D. (2017). A database for reproducible manipulation research: CapriDB – Capture, Print, Innovate. Data in Brief, 11, 491-498
Open this publication in new window or tab >>A database for reproducible manipulation research: CapriDB – Capture, Print, Innovate
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2017 (English)In: Data in Brief, ISSN 2352-3409, Vol. 11, p. 491-498Article in journal (Refereed) Published
Abstract [en]

We present a novel approach and database which combines the inexpensive generation of 3D object models via monocular or RGB-D camera images with 3D printing and a state of the art object tracking algorithm. Unlike recent efforts towards the creation of 3D object databases for robotics, our approach does not require expensive and controlled 3D scanning setups and aims to enable anyone with a camera to scan, print and track complex objects for manipulation research. The proposed approach results in detailed textured mesh models whose 3D printed replicas provide close approximations of the originals. A key motivation for utilizing 3D printed objects is the ability to precisely control and vary object properties such as the size, material properties and mass distribution in the 3D printing process to obtain reproducible conditions for robotic manipulation research. We present CapriDB – an extensible database resulting from this approach containing initially 40 textured and 3D printable mesh models together with tracking features to facilitate the adoption of the proposed approach.

Place, publisher, year, edition, pages
Elsevier, 2017
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-210103 (URN)10.1016/j.dib.2017.02.015 (DOI)2-s2.0-85014438696 (Scopus ID)
Note

QC 20170630

Available from: 2017-06-30 Created: 2017-06-30 Last updated: 2018-01-13Bibliographically approved
Hang, K., Stork, J. A., Pollard, N. S. & Kragic, D. (2017). A Framework for Optimal Grasp Contact Planning. IEEE Robotics and Automation Letters, 2(2), 704-711
Open this publication in new window or tab >>A Framework for Optimal Grasp Contact Planning
2017 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, p. 704-711Article in journal (Refereed) Published
Abstract [en]

We consider the problem of finding grasp contacts that are optimal under a given grasp quality function on arbitrary objects. Our approach formulates a framework for contact-level grasping as a path finding problem in the space of supercontact grasps. The initial supercontact grasp contains all grasps and in each step along a path grasps are removed. For this, we introduce and formally characterize search space structure and cost functions underwhich minimal cost paths correspond to optimal grasps. Our formulation avoids expensive exhaustive search and reduces computational cost by several orders of magnitude. We present admissible heuristic functions and exploit approximate heuristic search to further reduce the computational cost while maintaining bounded suboptimality for resulting grasps. We exemplify our formulation with point-contact grasping for which we define domain specific heuristics and demonstrate optimality and bounded suboptimality by comparing against exhaustive and uniform cost search on example objects. Furthermore, we explain how to restrict the search graph to satisfy grasp constraints for modeling hand kinematics. We also analyze our algorithm empirically in terms of created and visited search states and resultant effective branching factor.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2017
Keywords
Grasping, dexterous manipulation, multifingered hands, contact modeling
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-217455 (URN)10.1109/LRA.2017.2651381 (DOI)000413736600043 ()
Note

QC 20171117

Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2017-11-17Bibliographically approved
Güler, P., Pieropan, A., Ishikawa, M. & Kragic, D. (2017). Estimating deformability of objects using meshless shape matching. In: Bicchi, A Okamura, A (Ed.), 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), SEP 24-28, 2017, Vancouver, CANADA (pp. 5941-5948). IEEE
Open this publication in new window or tab >>Estimating deformability of objects using meshless shape matching
2017 (English)In: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) / [ed] Bicchi, A Okamura, A, IEEE , 2017, p. 5941-5948Conference paper, Published paper (Refereed)
Abstract [en]

Humans interact with deformable objects on a daily basis but this still represents a challenge for robots. To enable manipulation of and interaction with deformable objects, robots need to be able to extract and learn the deformability of objects both prior to and during the interaction. Physics-based models are commonly used to predict the physical properties of deformable objects and simulate their deformation accurately. The most popular simulation techniques are force-based models that need force measurements. In this paper, we explore the applicability of a geometry-based simulation method called meshless shape matching (MSM) for estimating the deformability of objects. The main advantages of MSM are its controllability and computational efficiency that make it popular in computer graphics to simulate complex interactions of multiple objects at the same time. Additionally, a useful feature of the MSM that differentiates it from other physics-based simulation is to be independent of force measurements that may not be available to a robotic framework lacking force/torque sensors. In this work, we design a method to estimate deformability based on certain properties, such as volume conservation. Using the finite element method (FEM) we create the ground truth deformability for various settings to evaluate our method. The experimental evaluation shows that our approach is able to accurately identify the deformability of test objects, supporting the value of MSM for robotic applications.

Place, publisher, year, edition, pages
IEEE, 2017
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-225807 (URN)000426978205083 ()978-1-5386-2682-5 (ISBN)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), SEP 24-28, 2017, Vancouver, CANADA
Funder
Swedish Foundation for Strategic Research
Note

QC 20180409

Available from: 2018-04-09 Created: 2018-04-09 Last updated: 2018-04-09Bibliographically approved
Bohg, J., Hausman, K., Sankaran, B., Brock, O., Kragic, D., Schaal, S. & Sukhatme, G. S. (2017). Interactive Perception: Leveraging Action in Perception and Perception in Action. IEEE Transactions on robotics, 33(6), 1273-1291
Open this publication in new window or tab >>Interactive Perception: Leveraging Action in Perception and Perception in Action
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2017 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 33, no 6, p. 1273-1291Article in journal (Refereed) Published
Abstract [en]

Recent approaches in robot perception follow the insight that perception is facilitated by interaction with the environment. These approaches are subsumed under the term Interactive Perception (IP). This view of perception provides the following benefits. First, interaction with the environment creates a rich sensory signal that would otherwise not be present. Second, knowledge of the regularity in the combined space of sensory data and action parameters facilitates the prediction and interpretation of the sensory signal. In this survey, we postulate this as a principle for robot perception and collect evidence in its support by analyzing and categorizing existing work in this area. We also provide an overview of the most important applications of IP. We close this survey by discussing remaining open questions. With this survey, we hope to help define the field of Interactive Perception and to provide a valuable resource for future research.

Place, publisher, year, edition, pages
IEEE, 2017
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-220617 (URN)10.1109/TRO.2017.2721939 (DOI)000417841500001 ()
Note

QC 20180112

Available from: 2018-01-12 Created: 2018-01-12 Last updated: 2018-02-26Bibliographically approved
Seita, D., Pokorny, F. T., Mahler, J., Kragic, D., Franklin, M., Canny, J. & Goldberg, K. (2017). Large-scale supervised learning of the grasp robustness of surface patch pairs. In: 2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2016: . Paper presented at 2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2016, 13 December 2016 through 16 December 2016 (pp. 216-223). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Large-scale supervised learning of the grasp robustness of surface patch pairs
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2017 (English)In: 2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2016, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 216-223Conference paper, Published paper (Refereed)
Abstract [en]

The robustness of a parallel-jaw grasp can be estimated by Monte Carlo sampling of perturbations in pose and friction but this is not computationally efficient. As an alternative, we consider fast methods using large-scale supervised learning, where the input is a description of a local surface patch at each of two contact points. We train and test with disjoint subsets of a corpus of 1.66 million grasps where robustness is estimated by Monte Carlo sampling using Dex-Net 1.0. We use the BIDMach machine learning toolkit to compare the performance of two supervised learning methods: Random Forests and Deep Learning. We find that both of these methods learn to estimate grasp robustness fairly reliably in terms of Mean Absolute Error (MAE) and ROC Area Under Curve (AUC) on a held-out test set. Speedups over Monte Carlo sampling are approximately 7500x for Random Forests and 1500x for Deep Learning.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2017
Keywords
Decision trees, Deep learning, Learning systems, Robot programming, Robots, Supervised learning, Computationally efficient, Disjoint subsets, Local surfaces, Mean absolute error, Monte Carlo sampling, Random forests, Supervised learning methods, Surface patches, Monte Carlo methods
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-207997 (URN)10.1109/SIMPAR.2016.7862399 (DOI)000405933700032 ()2-s2.0-85015928918 (Scopus ID)9781509046164 (ISBN)
Conference
2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2016, 13 December 2016 through 16 December 2016
Note

QC 20170608

Available from: 2017-06-08 Created: 2017-06-08 Last updated: 2017-11-10Bibliographically approved
Almeida, D., Ambrus, R., Caccamo, S., Chen, X., Cruciani, S., Pinto Basto De Carvalho, J. F., . . . Kragic, D. (2017). Team KTH’s Picking Solution for the Amazon Picking Challenge 2016. In: Warehouse Picking Automation Workshop 2017: Solutions, Experience, Learnings and Outlook of the Amazon Robotics Challenge. Paper presented at ICRA 2017.
Open this publication in new window or tab >>Team KTH’s Picking Solution for the Amazon Picking Challenge 2016
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2017 (English)In: Warehouse Picking Automation Workshop 2017: Solutions, Experience, Learnings and Outlook of the Amazon Robotics Challenge, 2017Conference paper, Oral presentation only (Other (popular science, discussion, etc.))
Abstract [en]

In this work we summarize the solution developed by Team KTH for the Amazon Picking Challenge 2016 in Leipzig, Germany. The competition simulated a warehouse automation scenario and it was divided in two tasks: a picking task where a robot picks items from a shelf and places them in a tote and a stowing task which is the inverse task where the robot picks items from a tote and places them in a shelf. We describe our approach to the problem starting from a high level overview of our system and later delving into details of our perception pipeline and our strategy for manipulation and grasping. The solution was implemented using a Baxter robot equipped with additional sensors.

National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-215327 (URN)
Conference
ICRA 2017
Note

QC 20171009

Available from: 2017-10-07 Created: 2017-10-07 Last updated: 2018-05-24Bibliographically approved
Ek, C. H. & Kragic, D. (2017). The importance of structure. In: 15th International Symposium of Robotics Research, 2011: . Paper presented at 9 December 2011 through 12 December 2011 (pp. 111-127). Springer
Open this publication in new window or tab >>The importance of structure
2017 (English)In: 15th International Symposium of Robotics Research, 2011, Springer, 2017, p. 111-127Conference paper, Published paper (Refereed)
Abstract [en]

Many tasks in robotics and computer vision are concerned with inferring a continuous or discrete state variable from observations and measurements from the environment. Due to the high-dimensional nature of the input data the inference is often cast as a two stage process: first a low-dimensional feature representation is extracted on which secondly a learning algorithm is applied. Due to the significant progress that have been achieved within the field of machine learning over the last decade focus have placed at the second stage of the inference process, improving the process by exploiting more advanced learning techniques applied to the same (or more of the same) data. We believe that for many scenarios significant strides in performance could be achieved by focusing on representation rather than aiming to alleviate inconclusive and/or redundant information by exploiting more advanced inference methods. This stems from the notion that; given the “correct” representation the inference problem becomes easier to solve. In this paper we argue that one important mode of information for many application scenarios is not the actual variation in the data but the rather the higher order statistics as the structure of variations. We will exemplify this through a set of applications and show different ways of representing the structure of data. © Springer International Publishing Switzerland 2017.

Place, publisher, year, edition, pages
Springer, 2017
Keywords
Artificial intelligence, Computer vision, Higher order statistics, Inference engines, Learning systems, Robotics, Advanced learning, Application scenario, Feature representation, Inference methods, Inference problem, Inference process, Observations and measurements, Two-stage process, Learning algorithms
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-195120 (URN)10.1007/978-3-319-29363-9_7 (DOI)2-s2.0-84984823812 (Scopus ID)9783319293622 (ISBN)
Conference
9 December 2011 through 12 December 2011
Note

Correspondence Address: Ek, C.H.; University of Bristol United Kingdom; email: carlhenrik.ek@bristol.ac.uk. QC 20161121

Available from: 2016-11-21 Created: 2016-11-02 Last updated: 2018-01-13Bibliographically approved
Högman, V., Björkman, M., Maki, A. & Kragic, D. (2016). A sensorimotor learning framework for object categorization. IEEE Transactions on Cognitive and Developmental Systems, 8(1), 15-25
Open this publication in new window or tab >>A sensorimotor learning framework for object categorization
2016 (English)In: IEEE Transactions on Cognitive and Developmental Systems, ISSN 2379-8920, Vol. 8, no 1, p. 15-25Article in journal (Refereed) Published
Abstract [en]

This paper presents a framework that enables a robot to discover various object categories through interaction. The categories are described using action-effect relations, i.e. sensorimotor contingencies rather than more static shape or appearance representation. The framework provides a functionality to classify objects and the resulting categories, associating a class with a specific module. We demonstrate the performance of the framework by studying a pushing behavior in robots, encoding the sensorimotor contingencies and their predictability with Gaussian Processes. We show how entropy-based action selection can improve object classification and how functional categories emerge from the similarities of effects observed among the objects. We also show how a multidimensional action space can be realized by parameterizing pushing using both position and velocity.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Keywords
sensorimotor learning, object classification, categorization, cognitive robotics, active perception, learning and adaptive system, embodiment, developmental robotics
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-172143 (URN)10.1109/TAMD.2015.2463728 (DOI)000388682400003 ()
Funder
Swedish Research CouncilEU, European Research Council, H2020-FETPROACT-2014 641321
Note

QC 20160422

Available from: 2016-04-21 Created: 2015-08-13 Last updated: 2017-01-04Bibliographically approved
Ghadirzadeh, A., Bütepage, J., Maki, A., Kragic, D. & Björkman, M. (2016). A sensorimotor reinforcement learning framework for physical human-robot interaction. In: IEEE International Conference on Intelligent Robots and Systems: . Paper presented at 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, 9 October 2016 through 14 October 2016 (pp. 2682-2688). IEEE
Open this publication in new window or tab >>A sensorimotor reinforcement learning framework for physical human-robot interaction
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2016 (English)In: IEEE International Conference on Intelligent Robots and Systems, IEEE, 2016, p. 2682-2688Conference paper, Published paper (Refereed)
Abstract [en]

Modeling of physical human-robot collaborations is generally a challenging problem due to the unpredictive nature of human behavior. To address this issue, we present a data-efficient reinforcement learning framework which enables a robot to learn how to collaborate with a human partner. The robot learns the task from its own sensorimotor experiences in an unsupervised manner. The uncertainty in the interaction is modeled using Gaussian processes (GP) to implement a forward model and an actionvalue function. Optimal action selection given the uncertain GP model is ensured by Bayesian optimization. We apply the framework to a scenario in which a human and a PR2 robot jointly control the ball position on a plank based on vision and force/torque data. Our experimental results show the suitability of the proposed method in terms of fast and data-efficient model learning, optimal action selection under uncertainty and equal role sharing between the partners.

Place, publisher, year, edition, pages
IEEE, 2016
Keywords
Behavioral research, Intelligent robots, Reinforcement learning, Robots, Bayesian optimization, Forward modeling, Gaussian process, Human behaviors, Human-robot collaboration, Model learning, Optimal actions, Physical human-robot interactions, Human robot interaction
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-202121 (URN)10.1109/IROS.2016.7759417 (DOI)000391921702127 ()2-s2.0-85006367922 (Scopus ID)9781509037629 (ISBN)
Conference
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, 9 October 2016 through 14 October 2016
Note

QC 20170228

Available from: 2017-02-28 Created: 2017-02-28 Last updated: 2018-05-21Bibliographically approved
Caccamo, S., Bekiroglu, Y., Ek, C. H. & Kragic, D. (2016). Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces. In: 2016 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2016): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), OCT 09-14, 2016, Daejeon, SOUTH KOREA (pp. 582-589). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces
2016 (English)In: 2016 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2016), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 582-589Conference paper, Published paper (Refereed)
Abstract [en]

In this work we study the problem of exploring surfaces and building compact 3D representations of the environment surrounding a robot through active perception. We propose an online probabilistic framework that merges visual and tactile measurements using Gaussian Random Field and Gaussian Process Implicit Surfaces. The system investigates incomplete point clouds in order to find a small set of regions of interest which are then physically explored with a robotic arm equipped with tactile sensors. We show experimental results obtained using a PrimeSense camera, a Kinova Jaco2 robotic arm and Optoforce sensors on different scenarios. We then demostrate how to use the online framework for object detection and terrain classification.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Keywords
Active perception, Surface reconstruction, Gaussian process, Implicit surface, Random field, Tactile exploration
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-202672 (URN)10.1109/IROS.2016.7759112 (DOI)000391921700086 ()2-s2.0-85006371409 (Scopus ID)978-1-5090-3762-9 (ISBN)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), OCT 09-14, 2016, Daejeon, SOUTH KOREA
Note

QC 20170306

Available from: 2017-03-06 Created: 2017-03-06 Last updated: 2018-04-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2965-2953

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