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Publications (10 of 32) Show all publications
Krishnan, S., Garg, A., Liaw, R., Thananjeyan, B., Miller, L., Pokorny, F. T. & Goldberg, K. (2019). SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards. The international journal of robotics research, 38(2-3), 126-145
Open this publication in new window or tab >>SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards
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2019 (English)In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 38, no 2-3, p. 126-145Article in journal (Refereed) Published
Abstract [en]

We present sequential windowed inverse reinforcement learning (SWIRL), a policy search algorithm that is a hybrid of exploration and demonstration paradigms for robot learning. We apply unsupervised learning to a small number of initial expert demonstrations to structure future autonomous exploration. SWIRL approximates a long time horizon task as a sequence of local reward functions and subtask transition conditions. Over this approximation, SWIRL applies Q-learning to compute a policy that maximizes rewards. Experiments suggest that SWIRL requires significantly fewer rollouts than pure reinforcement learning and fewer expert demonstrations than behavioral cloning to learn a policy. We evaluate SWIRL in two simulated control tasks, parallel parking and a two-link pendulum. On the parallel parking task, SWIRL achieves the maximum reward on the task with 85% fewer rollouts than Q-learning, and one-eight of demonstrations needed by behavioral cloning. We also consider physical experiments on surgical tensioning and cutting deformable sheets using a da Vinci surgical robot. On the deformable tensioning task, SWIRL achieves a 36% relative improvement in reward compared with a baseline of behavioral cloning with segmentation.

Place, publisher, year, edition, pages
SAGE PUBLICATIONS LTD, 2019
Keywords
Reinforcement learning, inverse reinforcement learning, learning from demonstrations, medical robots and systems
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-247830 (URN)10.1177/0278364918784350 (DOI)000460099500003 ()2-s2.0-85052190280 (Scopus ID)
Note

QC 20190326

Available from: 2019-03-26 Created: 2019-03-26 Last updated: 2019-03-26Bibliographically approved
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 ()2-s2.0-85074143181 (Scopus ID)
Funder
Knut and Alice Wallenberg FoundationSwedish Research Council
Note

QC 20180725

Available from: 2018-07-25 Created: 2018-07-25 Last updated: 2019-12-02Bibliographically 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
Mahler, J., Pokorny, F. T., Niyaz, S. & Goldberg, K. (2018). Synthesis of Energy-Bounded Planar Caging Grasps Using Persistent Homology. IEEE Transactions on Automation Science and Engineering, 15(3), 908-918
Open this publication in new window or tab >>Synthesis of Energy-Bounded Planar Caging Grasps Using Persistent Homology
2018 (English)In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 15, no 3, p. 908-918Article in journal (Refereed) Published
Abstract [en]

For applications such as manufacturing, caging grasps restrict object motion without requiring complete immobilization, providing a robust alternative to force-and form-closure grasps. Energy-bounded cages are a new class of caging grasps that relax the requirement of complete caging in the presence of external forces such as gravity or constant velocity pushing in the horizontal plane with Coulomb friction. We address the problem of synthesizing planar energy-bounded cages by identifying gripper and force-direction configurations that maximize the energy required for the object to escape. We present Energy-Bounded-Cage-Synthesis-2-D (EBCS-2-D), a sampling-based algorithm that uses persistent homology, a recently-developed multiscale approach for topological analysis, to efficiently compute candidate rigid configurations of obstacles that form energy-bounded cages of an object from an alpha-shape approximation to the configuration space. If a synthesized configuration has infinite escape energy then the object is completely caged. EBCS-2-D runs in O(s(3) + sn(2)) time, where s is the number of samples and n is the number of object and obstacle vertices, where typically n << s. We observe runtimes closer to O(s) for fixed n. We implement EBCS-2-D using the persistent homology algorithms toolbox and study performance on a set of seven planar objects and four gripper types. Experiments suggest that EBCS-2-D takes 2-3 min on a 6 core processor with 200 000 pose samples. We also confirm that an rapidly-exploring random tree* motion planner is unable to find escape paths with lower energy. Physical experiments on a five degree of freedom Zymark Zymate and ABB YuMi suggest that push grasps synthesized by EBCS-2-D are robust to perturbations. Data and code are available at http://berkeleyautomation.github.io/caging/.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2018
Keywords
Computational geometry, motion planning, robots, topology
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-232399 (URN)10.1109/TASE.2018.2831724 (DOI)000437415300002 ()2-s2.0-85047009632 (Scopus ID)
Funder
Knut and Alice Wallenberg Foundation
Note

QC 20180726

Available from: 2018-07-26 Created: 2018-07-26 Last updated: 2018-07-26Bibliographically 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
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
Hawasly, M., Pokorny, F. T. & Ramamoorthy, S. (2017). Multi-scale activity estimation with spatial abstractions. In: 3rd International Conference on Geometric Science of Information, GSI 2017: . Paper presented at 3rd International Conference on Geometric Science of Information, GSI 2017, Paris, France, 7 November 2017 through 9 November 2017 (pp. 273-281). Springer, 10589
Open this publication in new window or tab >>Multi-scale activity estimation with spatial abstractions
2017 (English)In: 3rd International Conference on Geometric Science of Information, GSI 2017, Springer, 2017, Vol. 10589, p. 273-281Conference paper, Published paper (Refereed)
Abstract [en]

Estimation and forecasting of dynamic state are fundamental to the design of autonomous systems such as intelligent robots. State-of-the-art algorithms, such as the particle filter, face computational limitations when needing to maintain beliefs over a hypothesis space that is made large by the dynamic nature of the environment. We propose an algorithm that utilises a hierarchy of such filters, exploiting a filtration arising from the geometry of the underlying hypothesis space. In addition to computational savings, such a method can accommodate the availability of evidence at varying degrees of coarseness. We show, using synthetic trajectory datasets, that our method achieves a better normalised error in prediction and better time to convergence to a true class when compared against baselines that do not similarly exploit geometric structure.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10589
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-218313 (URN)10.1007/978-3-319-68445-1_32 (DOI)000440482500032 ()2-s2.0-85033660648 (Scopus ID)9783319684444 (ISBN)
Conference
3rd International Conference on Geometric Science of Information, GSI 2017, Paris, France, 7 November 2017 through 9 November 2017
Note

QC 20171127

Available from: 2017-11-27 Created: 2017-11-27 Last updated: 2018-08-15Bibliographically approved
Varava, A., Kragic, D. & Pokorny, F. T. (2016). Caging Grasps of Rigid and Partially Deformable 3-D Objects With Double Fork and Neck Features. IEEE Transactions on robotics, 32(6), 1479-1497
Open this publication in new window or tab >>Caging Grasps of Rigid and Partially Deformable 3-D Objects With Double Fork and Neck Features
2016 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 32, no 6, p. 1479-1497Article in journal (Refereed) Published
Abstract [en]

Caging provides an alternative to point-contact-based rigid grasping, relying on reasoning about the global free configuration space of an object under consideration. While substantial progress has been made toward the analysis, verification, and synthesis of cages of polygonal objects in the plane, the use of caging as a tool for manipulating general complex objects in 3-D remains challenging. In this work, we introduce the problem of caging rigid and partially deformable 3-D objects, which exhibit geometric features we call double forks and necks. Our approach is based on the linking number-a classical topological invariant, allowing us to determine sufficient conditions for caging objects with these features even in the case when the object under consideration is partially deformable under a set of neck or double fork preserving deformations. We present synthesis and verification algorithms and demonstrations of applying these algorithms to cage 3-D meshes.

Keywords
Cage, grasping, shape features
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-199500 (URN)10.1109/TRO.2016.2602374 (DOI)000389849700012 ()2-s2.0-85006017877 (Scopus ID)
Note

QC 20170118

Available from: 2017-01-18 Created: 2017-01-09 Last updated: 2019-04-01Bibliographically approved
Hang, K., Li, M., Stork, J. A., Bekiroglu, Y., Pokorny, F. T., Billard, A. & Kragic, D. (2016). Hierarchical Fingertip Space: A Unified Framework for Grasp Planning and In-Hand Grasp Adaptation. IEEE Transactions on robotics, 32(4), 960-972, Article ID 7530865.
Open this publication in new window or tab >>Hierarchical Fingertip Space: A Unified Framework for Grasp Planning and In-Hand Grasp Adaptation
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2016 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 32, no 4, p. 960-972, article id 7530865Article in journal (Refereed) Published
Abstract [en]

We present a unified framework for grasp planning and in-hand grasp adaptation using visual, tactile and proprioceptive feedback. The main objective of the proposed framework is to enable fingertip grasping by addressing problems of changed weight of the object, slippage and external disturbances. For this purpose, we introduce the Hierarchical Fingertip Space (HFTS) as a representation enabling optimization for both efficient grasp synthesis and online finger gaiting. Grasp synthesis is followed by a grasp adaptation step that consists of both grasp force adaptation through impedance control and regrasping/finger gaiting when the former is not sufficient. Experimental evaluation is conducted on an Allegro hand mounted on a Kuka LWR arm.

Place, publisher, year, edition, pages
IEEE Press, 2016
Keywords
Hierarchical Fingertip Space, Grasp Planning, Grasp Adaptation, Fingertip Grasping
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-187058 (URN)10.1109/TRO.2016.2588879 (DOI)000382754900016 ()2-s2.0-84981303220 (Scopus ID)
Projects
FlexBot
Funder
EU, European Research Council, FLEXBOT - FP7-ERC-279933
Note

QC 20160517

Available from: 2016-05-16 Created: 2016-05-16 Last updated: 2017-11-30Bibliographically approved
Pokorny, F. T., Hawasly, M. & Ramamoorthy, S. (2016). Topological trajectory classification with filtrations of simplicial complexes and persistent homology. The international journal of robotics research, 35(1-3), 204-223
Open this publication in new window or tab >>Topological trajectory classification with filtrations of simplicial complexes and persistent homology
2016 (English)In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 35, no 1-3, p. 204-223Article in journal (Refereed) Published
Abstract [en]

In this work, we present a sampling-based approach to trajectory classification which enables automated high-level reasoning about topological classes of trajectories. Our approach is applicable to general configuration spaces and relies only on the availability of collision free samples. Unlike previous sampling-based approaches in robotics which use graphs to capture information about the path-connectedness of a configuration space, we construct a multiscale approximation of neighborhoods of the collision free configurations based on filtrations of simplicial complexes. Our approach thereby extracts additional homological information which is essential for a topological trajectory classification. We propose a multiscale classification algorithm for trajectories in configuration spaces of arbitrary dimension and for sets of trajectories starting and ending in two fixed points. Using a cone construction, we then generalize this approach to classify sets of trajectories even when trajectory start and end points are allowed to vary in path-connected subsets. We furthermore show how an augmented filtration of simplicial complexes based on an arbitrary function on the configuration space, such as a costmap, can be defined to incorporate additional constraints. We present an evaluation of our approach in 2-, 3-, 4- and 6-dimensional configuration spaces in simulation and in real-world experiments using a Baxter robot and motion capture data.

Place, publisher, year, edition, pages
Sage Publications, 2016
Keywords
motion classification, Persistent homology, topological robotics
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-181447 (URN)10.1177/0278364915586713 (DOI)000368032600012 ()2-s2.0-84953282328 (Scopus ID)
Note

QC 20160203

Available from: 2016-02-03 Created: 2016-02-02 Last updated: 2017-11-30Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1114-6040

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