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Publications (9 of 9) Show all publications
Cruciani, S., Hang, K., Smith, C. & Kragic, D. (2019). Dual-Arm In-Hand Manipulation Using Visual Feedback. In: : . Paper presented at IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids) October 15-17, 2019 Toronto, Canada (pp. 411-418).
Open this publication in new window or tab >>Dual-Arm In-Hand Manipulation Using Visual Feedback
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this work, we address the problem of executing in-hand manipulation based on visual input. Given an initial grasp, the robot has to change its grasp configuration without releasing the object. We propose a method for in-hand manipulation planning and execution based on information on the object’s shape using a dual-arm robot. From the available information on the object, which can be a complete point cloud but also partial data, our method plans a sequence of rotations and translations to reconfigure the object’s pose. This sequence is executed using non-prehensile pushes defined as relative motions between the two robot arms.

National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-262881 (URN)
Conference
IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids) October 15-17, 2019 Toronto, Canada
Note

QC 20191129

Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-11-29Bibliographically approved
Haustein, J. A., Hang, K., Stork, J. A. & Kragic, D. (2019). Object Placement Planning and Optimization for Robot Manipulators. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019): . Paper presented at International Conference on Intelligent Robots and Systems (IROS), Macau, China, November 4-8, 2019.
Open this publication in new window or tab >>Object Placement Planning and Optimization for Robot Manipulators
2019 (English)In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), 2019Conference paper, Published paper (Refereed)
Abstract [en]

We address the problem of planning the placement of a rigid object with a dual-arm robot in a cluttered environment. In this task, we need to locate a collision-free pose for the object that a) facilitates the stable placement of the object, b) is reachable by the robot and c) optimizes a user-given placement objective. In addition, we need to select which robot arm to perform the placement with. To solve this task, we propose an anytime algorithm that integrates sampling-based motion planning with a novel hierarchical search for suitable placement poses. Our algorithm incrementally produces approach motions to stable placement poses, reaching placements with better objective as runtime progresses. We evaluate our approach for two different placement objectives, and observe its effectiveness even in challenging scenarios.

Keywords
Motion planning, Object placing
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-264015 (URN)
Conference
International Conference on Intelligent Robots and Systems (IROS), Macau, China, November 4-8, 2019
Funder
Swedish Foundation for Strategic Research Knut and Alice Wallenberg Foundation
Note

QC 20191210

Available from: 2019-11-20 Created: 2019-11-20 Last updated: 2020-01-31Bibliographically approved
Haustein, J. A., Cruciani, S., Asif, R., Hang, K. & Kragic, D. (2019). Placing Objects with prior In-Hand Manipulation using Dexterous Manipulation Graphs. In: : . Paper presented at IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids), Toronto, Canada, October 15-17, 2019. (pp. 477-484).
Open this publication in new window or tab >>Placing Objects with prior In-Hand Manipulation using Dexterous Manipulation Graphs
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2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We address the problem of planning the placement of a grasped object with a robot manipulator. More specifically, the robot is tasked to place the grasped object such that a placement preference function is maximized. For this, we present an approach that uses in-hand manipulation to adjust the robot’s initial grasp to extend the set of reachable placements. Given an initial grasp, the algorithm computes a set of grasps that can be reached by pushing and rotating the object in-hand. With this set of reachable grasps, it then searches for a stable placement that maximizes the preference function. If successful it returns a sequence of in-hand pushes to adjust the initial grasp to a more advantageous grasp together with a transport motion that carries the object to the placement. We evaluate our algorithm’s performance on various placing scenarios, and observe its effectiveness also in challenging scenes containing many obstacles. Our experiments demonstrate that re-grasping with in-hand manipulation increases the quality of placements the robot can reach. In particular, it enables the algorithm to find solutions in situations where safe placing with the initial grasp wouldn’t be possible.

National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-262882 (URN)
Conference
IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids), Toronto, Canada, October 15-17, 2019.
Note

QC 20191115

Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2020-01-22Bibliographically approved
Haustein, J. A., Arnekvist, I., Stork, J. A., Hang, K. & Kragic, D. (2018). Non-prehensile Rearrangement Planning with Learned Manipulation States and Actions. In: Workshop on "Machine Learning in Robot Motion Planning" at the International Conference on Intelligent Robots and Systems (IROS) 2018: . Paper presented at Workshop on "Machine Learning in Robot Motion Planning", International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 5 2018.
Open this publication in new window or tab >>Non-prehensile Rearrangement Planning with Learned Manipulation States and Actions
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2018 (English)In: Workshop on "Machine Learning in Robot Motion Planning" at the International Conference on Intelligent Robots and Systems (IROS) 2018, 2018Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

n this work we combine sampling-based motionplanning with reinforcement learning and generative modelingto solve non-prehensile rearrangement problems. Our algorithmexplores the composite configuration space of objects and robotas a search over robot actions, forward simulated in a physicsmodel. This search is guided by a generative model thatprovides robot states from which an object can be transportedtowards a desired state, and a learned policy that providescorresponding robot actions. As an efficient generative model,we apply Generative Adversarial Networks.

Keywords
Rearrangement planning, Pushing, Generative Adversarial Models
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-264024 (URN)
Conference
Workshop on "Machine Learning in Robot Motion Planning", International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 5 2018
Note

QCR 20191210

Available from: 2019-11-20 Created: 2019-11-20 Last updated: 2020-01-31Bibliographically 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 ()2-s2.0-85050300542 (Scopus ID)
Note

QC 20171117

Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2020-03-09Bibliographically approved
Hang, K. (2016). Dexterous Grasping: Representation and Optimization. (Doctoral dissertation). Stockholm: KTH Royal Institute of Technology
Open this publication in new window or tab >>Dexterous Grasping: Representation and Optimization
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Many robot object interactions require that an object is firmly held, and that the grasp remains stable during the whole manipulation process. Based on grasp wrench space, this thesis address the problems of measuring the grasp sensitivity against friction changes, planning contacts and hand configurations on mesh and point cloud representations of arbitrary objects, planning adaptable grasps and finger gaiting for keeping a grasp stable under various external disturbances, as well as learning of grasping manifolds for more accurate reachability and inverse kinematics computation for multifingered grasping. 

Firstly, we propose a new concept called friction sensitivity, which measures how susceptible a specific grasp is to changes in the underlying frictionc oefficients. We develop algorithms for the synthesis of stable grasps with low friction sensitivity and for the synthesis of stable grasps in the case of small friction coefficients.  

Secondly, for fast planning of contacts and hand configurations for dexterous grasping, as well as keeping the stability of a grasp during execution, 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. 

Lastly, to improve the efficiency and accuracy of dexterous grasping and in-hand manipulation, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reachability and multi-fingered inverse kinematics. During execution the system plans and executes fingertip grasps using Canny’s grasp quality metric and a learned random forest based hand reachability heuristic. In the offline module, this heuristic is improved based on a grasping manifold that is incrementally learned from the experiences collected during execution.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. p. 167
Series
TRITA-CSC-A, ISSN 1653-5723 ; 14
Keywords
Dexterous Grasping, Hierarchical Fingertip Space, Grasp Planning, Grasp Adaptation
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-186158 (URN)978-91-7595-993-1 (ISBN)
Public defence
2016-06-03, D2, Lindstedtsvägen 5, Stockholm, 13:25 (English)
Opponent
Projects
Flexbot
Funder
EU, European Research Council, 6138
Note

QC 20160516

Available from: 2016-05-16 Created: 2016-05-03 Last updated: 2016-05-18Bibliographically 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
Haustein, J. A., Arnekvist, I., Stork, J. A., Hang, K. & Kragic, D.Learning Manipulation States and Actions for Efficient Non-prehensile Rearrangement Planning.
Open this publication in new window or tab >>Learning Manipulation States and Actions for Efficient Non-prehensile Rearrangement Planning
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper addresses non-prehensile rearrangement planning problems where a robot is tasked to rearrange objects among obstacles on a planar surface. We present an efficient planning algorithm that is designed to impose few assumptions on the robot's non-prehensile manipulation abilities and is simple to adapt to different robot embodiments. For this, we combine sampling-based motion planning with reinforcement learning and generative modeling. Our algorithm explores the composite configuration space of objects and robot as a search over robot actions, forward simulated in a physics model. This search is guided by a generative model that provides robot states from which an object can be transported towards a desired state, and a learned policy that provides corresponding robot actions. As an efficient generative model, we apply Generative Adversarial Networks. We implement and evaluate our approach for robots endowed with configuration spaces in SE(2). We demonstrate empirically the efficacy of our algorithm design choices and observe more than 2x speedup in planning time on various test scenarios compared to a state-of-the-art approach.

Keywords
Rearrangement planning, manipulation planning, robot pushing, generative adversarial networks
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-266790 (URN)
Note

QC 20200127

Available from: 2020-01-22 Created: 2020-01-22 Last updated: 2020-01-31Bibliographically approved
Song, H., Haustein, J. A., Yuan, W., Hang, K., Wang, M. Y., Kragic, D. & Stork, J. A.Multi-Object Rearrangement with Monte Carlo Tree Search: A Case Study on Planar Nonprehensile Sorting.
Open this publication in new window or tab >>Multi-Object Rearrangement with Monte Carlo Tree Search: A Case Study on Planar Nonprehensile Sorting
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

In this work, we address a planar non-prehensile sorting task. Here, a robot needs to push many densely packed objects belonging to different classes into a configuration where these classes are clearly separated from each other. To achieve this, we propose to employ Monte Carlo tree search equipped with a task-specific heuristic function. We evaluate the algorithm on various simulated sorting tasks and observe its effectiveness in reliably sorting up to 40 convex objects. In addition, we observe that the algorithm is capable to also sort non-convex objects, as well as convex objects in the presence of immovable obstacles.

National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-266783 (URN)
Note

QC 20200124

Available from: 2020-01-21 Created: 2020-01-21 Last updated: 2020-01-31Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4132-1217

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