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Publications (10 of 263) Show all publications
Sibirtseva, E., Kontogiorgos, D., Nykvist, O., Karaoguz, H., Leite, I., Gustafson, J. & Kragic, D. (2018). A Comparison of Visualisation Methods for Disambiguating Verbal Requests in Human-Robot Interaction. In: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN): . Paper presented at ROMAN 2018.
Open this publication in new window or tab >>A Comparison of Visualisation Methods for Disambiguating Verbal Requests in Human-Robot Interaction
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2018 (English)In: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2018Conference paper, Published paper (Refereed)
Abstract [en]

Picking up objects requested by a human user is a common task in human-robot interaction. When multiple objects match the user's verbal description, the robot needs to clarify which object the user is referring to before executing the action. Previous research has focused on perceiving user's multimodal behaviour to complement verbal commands or minimising the number of follow up questions to reduce task time. In this paper, we propose a system for reference disambiguation based on visualisation and compare three methods to disambiguate natural language instructions. In a controlled experiment with a YuMi robot, we investigated realtime augmentations of the workspace in three conditions - head-mounted display, projector, and a monitor as the baseline - using objective measures such as time and accuracy, and subjective measures like engagement, immersion, and display interference. Significant differences were found in accuracy and engagement between the conditions, but no differences were found in task time. Despite the higher error rates in the head-mounted display condition, participants found that modality more engaging than the other two, but overall showed preference for the projector condition over the monitor and head-mounted display conditions.

National Category
Human Computer Interaction
Identifiers
urn:nbn:se:kth:diva-235548 (URN)10.1109/ROMAN.2018.8525554 (DOI)978-1-5386-7981-4 (ISBN)
Conference
ROMAN 2018
Note

QC 20181207

Available from: 2018-09-29 Created: 2018-09-29 Last updated: 2018-12-07Bibliographically approved
Pinto Basto de Carvalho, J. F., Vejdemo-Johansson, M., Kragic, D. & Pokorny, F. T. (2018). An algorithm for calculating top-dimensional bounding chains. PEERJ COMPUTER SCIENCE, Article ID e153.
Open this publication in new window or tab >>An algorithm for calculating top-dimensional bounding chains
2018 (English)In: PEERJ COMPUTER SCIENCE, ISSN 2376-5992, article id e153Article in journal (Refereed) Published
Abstract [en]

We describe the Coefficient-Flow algorithm for calculating the bounding chain of an (n-1)-boundary on an n-manifold-like simplicial complex S. We prove its correctness and show that it has a computational time complexity of O(vertical bar S(n-1)vertical bar) (where S(n-1) is the set of (n-1)-faces of S). We estimate the big-O coefficient which depends on the dimension of S and the implementation. We present an implementation, experimentally evaluate the complexity of our algorithm, and compare its performance with that of solving the underlying linear system.

Place, publisher, year, edition, pages
PEERJ INC, 2018
Keywords
Homology, Computational algebraic topology
National Category
Computational Mathematics
Identifiers
urn:nbn:se:kth:diva-232420 (URN)10.7717/peerj-cs.153 (DOI)000437236300001 ()
Funder
Knut and Alice Wallenberg FoundationSwedish Research Council
Note

QC 20180725

Available from: 2018-07-25 Created: 2018-07-25 Last updated: 2018-07-25Bibliographically approved
Butepage, J., Kjellström, H. & Kragic, D. (2018). Anticipating many futures: Online human motion prediction and generation for human-robot interaction. In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA): . Paper presented at IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA (pp. 4563-4570). IEEE COMPUTER SOC
Open this publication in new window or tab >>Anticipating many futures: Online human motion prediction and generation for human-robot interaction
2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE COMPUTER SOC , 2018, p. 4563-4570Conference paper, Published paper (Refereed)
Abstract [en]

Fluent and safe interactions of humans and robots require both partners to anticipate the others' actions. The bottleneck of most methods is the lack of an accurate model of natural human motion. In this work, we present a conditional variational autoencoder that is trained to predict a window of future human motion given a window of past frames. Using skeletal data obtained from RGB depth images, we show how this unsupervised approach can be used for online motion prediction for up to 1660 ms. Additionally, we demonstrate online target prediction within the first 300-500 ms after motion onset without the use of target specific training data. The advantage of our probabilistic approach is the possibility to draw samples of possible future motion patterns. Finally, we investigate how movements and kinematic cues are represented on the learned low dimensional manifold.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC, 2018
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-237164 (URN)000446394503071 ()978-1-5386-3081-5 (ISBN)
Conference
IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA
Funder
Swedish Foundation for Strategic Research
Note

QC 20181024

Available from: 2018-10-24 Created: 2018-10-24 Last updated: 2018-10-24Bibliographically approved
Krug, R., Bekiroglu, Y., Kragic, D. & Roa, M. A. (2018). Evaluating the Quality of Non-Prehensile Balancing Grasps. In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA): . Paper presented at IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA (pp. 4215-4220). IEEE Computer Society
Open this publication in new window or tab >>Evaluating the Quality of Non-Prehensile Balancing Grasps
2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE Computer Society, 2018, p. 4215-4220Conference paper, Published paper (Refereed)
Abstract [en]

Assessing grasp quality and, subsequently, predicting grasp success is useful for avoiding failures in many autonomous robotic applications. In addition, interest in non-prehensile grasping and manipulation has been growing as it offers the potential for a large increase in dexterity. However, while force-closure grasping has been the subject of intense study for many years, few existing works have considered quality metrics for non-prehensile grasps. Furthermore, no studies exist to validate them in practice. In this work we use a real-world data set of non-prehensile balancing grasps and use it to experimentally validate a wrench-based quality metric by means of its grasp success prediction capability. The overall accuracy of up to 84% is encouraging and in line with existing results for force-closure grasps.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-237163 (URN)000446394503032 ()978-1-5386-3081-5 (ISBN)
Conference
IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA
Funder
Swedish Foundation for Strategic Research
Note

QC 20181024

Available from: 2018-10-24 Created: 2018-10-24 Last updated: 2018-10-24Bibliographically approved
Kragic, D. (2018). From active perception to deep learning. SCIENCE ROBOTICS, 3(23), Article ID eaav1778.
Open this publication in new window or tab >>From active perception to deep learning
2018 (English)In: SCIENCE ROBOTICS, ISSN 2470-9476, Vol. 3, no 23, article id eaav1778Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
AMER ASSOC ADVANCEMENT SCIENCE, 2018
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-239087 (URN)10.1126/scirobotics.aav1778 (DOI)000448624000004 ()
Note

QC 20181121

Available from: 2018-11-21 Created: 2018-11-21 Last updated: 2018-11-21Bibliographically approved
Carvalho, J. F., Vejdemo-Johansson, M., Kragic, D. & Pokorny, F. T. (2018). Path Clustering with Homology Area. In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA): . Paper presented at IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA (pp. 7346-7353). IEEE Computer Society
Open this publication in new window or tab >>Path Clustering with Homology Area
2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE Computer Society, 2018, p. 7346-7353Conference paper, Published paper (Refereed)
Abstract [en]

Path clustering has found many applications in recent years. Common approaches to this problem use aggregates of the distances between points to provide a measure of dissimilarity between paths which do not satisfy the triangle inequality. Furthermore, they do not take into account the topology of the space where the paths are embedded. To tackle this, we extend previous work in path clustering with relative homology, by employing minimum homology area as a measure of distance between homologous paths in a triangulated mesh. Further, we show that the resulting distance satisfies the triangle inequality, and how we can exploit the properties of homology to reduce the amount of pairwise distance calculations necessary to cluster a set of paths. We further compare the output of our algorithm with that of DTW on a toy dataset of paths, as well as on a dataset of real-world paths.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Geometry
Identifiers
urn:nbn:se:kth:diva-237170 (URN)000446394505086 ()978-1-5386-3081-5 (ISBN)
Conference
IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA
Note

QC 20181024

Available from: 2018-10-24 Created: 2018-10-24 Last updated: 2018-10-24Bibliographically approved
Yuan, W., Stork, J. A., Kragic, D., Wang, M. Y. & Hang, K. (2018). Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning. In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA): . Paper presented at IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA (pp. 270-277). IEEE Computer Society
Open this publication in new window or tab >>Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning
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2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE Computer Society, 2018, p. 270-277Conference paper, Published paper (Refereed)
Abstract [en]

Rearranging objects on a tabletop surface by means of nonprehensile manipulation is a task which requires skillful interaction with the physical world. Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning. In contrast, as explicitly modeling the physical environment is not always feasible and involves various uncertainties, we learn a nonprehensile rearrangement strategy with deep reinforcement learning based on only visual feedback. For this, we model the task with rewards and train a deep Q-network. Our potential field-based heuristic exploration strategy reduces the amount of collisions which lead to suboptimal outcomes and we actively balance the training set to avoid bias towards poor examples. Our training process leads to quicker learning and better performance on the task as compared to uniform exploration and standard experience replay. We demonstrate empirical evidence from simulation that our method leads to a success rate of 85%, show that our system can cope with sudden changes of the environment, and compare our performance with human level performance.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-237158 (URN)000446394500028 ()978-1-5386-3081-5 (ISBN)
Conference
IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA
Funder
Knut and Alice Wallenberg Foundation
Note

QC 20181024

Available from: 2018-10-24 Created: 2018-10-24 Last updated: 2018-10-24Bibliographically approved
Yang, G.-Z., Dario, P. & Kragic, D. (2018). Social robotics-Trust, learning, and social interaction. Science Robotics, 3(21), Article ID UNSP eaau8839.
Open this publication in new window or tab >>Social robotics-Trust, learning, and social interaction
2018 (English)In: Science Robotics, ISSN 2470-9476, Vol. 3, no 21, article id UNSP eaau8839Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
American Association for the Advancement of Science, 2018
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-240200 (URN)10.1126/scirobotics.aau8839 (DOI)000443232300009 ()
Note

QC 20181219

Available from: 2018-12-19 Created: 2018-12-19 Last updated: 2018-12-19Bibliographically approved
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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2965-2953

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