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  • 1.
    Cruciani, Silvia
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Hang, Kaiyu
    Yale University.
    Smith, Christian
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Dual-Arm In-Hand Manipulation Using Visual Feedback2019Conference 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.

  • 2.
    Hang, Kaiyu
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. CVAP/CAS/CSC, KTH Royal Institute of Technology.
    Dexterous Grasping: Representation and Optimization2016Doctoral 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.

  • 3.
    Hang, Kaiyu
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Haustein, Joshua
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Li, Miao
    EPFL.
    Billard, Aude
    Smith, Christian
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    On the Evolution of Fingertip Grasping Manifolds2016In: IEEE International Conference on Robotics and Automation, IEEE Robotics and Automation Society, 2016, p. 2022-2029, article id 7487349Conference paper (Refereed)
    Abstract [en]

    Efficient and accurate planning of fingertip grasps is essential for dexterous in-hand manipulation. In this work, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reachability and multi-fingered inverse kinematics. The system consists of an online execution module and an offline optimization module. 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. The system is evaluated both in simulation and on a SchunkSDH dexterous hand mounted on a KUKA-KR5 arm. We show that, as the grasping manifold is adapted to the system’s experiences, the heuristic becomes more accurate, which results in an improved performance of the execution module. The improvement is not only observed for experienced objects, but also for previously unknown objects of similar sizes.

  • 4.
    Hang, Kaiyu
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Li, Miao
    Stork, Johannes A.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Bekiroglu, Yasemin
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Billard, Aude
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Hierarchical Fingertip Space for Synthesizing Adaptable Fingertip Grasps2014Conference paper (Refereed)
  • 5.
    Hang, Kaiyu
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Li, Miao
    EPFL.
    Stork, Johannes A.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Bekiroglu, Yasemin
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Pokorny, Florian T.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Billard, Aude
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Hierarchical Fingertip Space: A Unified Framework for Grasp Planning and In-Hand Grasp Adaptation2016In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 32, no 4, p. 960-972, article id 7530865Article in journal (Refereed)
    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.

  • 6.
    Hang, Kaiyu
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Pokorny, Florian T.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Friction Coefficients and Grasp Synthesis2013In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), IEEE , 2013, p. 3520-3526Conference paper (Refereed)
    Abstract [en]

    We propose a new concept called friction sensitivity which measures how susceptible a specific grasp is to changes in the underlying friction coefficients. 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. We describe how grasps with low friction sensitivity can be used when a robot has an uncertain belief about friction coefficients and study the statistics of grasp quality under changes in those coefficients. We also provide a parametric estimate for the distribution of grasp qualities and friction sensitivities for a uniformly sampled set of grasps.

  • 7.
    Hang, Kaiyu
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Stork, Johannes A.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Hierarchical Fingertip Space for Multi-fingered Precision Grasping2014In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2014), IEEE , 2014, p. 1641-1648Conference paper (Refereed)
    Abstract [en]

    Dexterous in-hand manipulation of objects benefits from the ability of a robot system to generate precision grasps. In this paper, we propose a concept of Fingertip Space and its use for precision grasp synthesis. Fingertip Space is a representation that takes into account both the local geometry of object surface as well as the fingertip geometry. As such, it is directly applicable to the object point cloud data and it establishes a basis for the grasp search space. We propose a model for a hierarchical encoding of the Fingertip Space that enables multilevel refinement for efficient grasp synthesis. The proposed method works at the grasp contact level while not neglecting object shape nor hand kinematics. Experimental evaluation is performed for the Barrett hand considering also noisy and incomplete point cloud data.

  • 8.
    Hang, Kaiyu
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Stork, Johannes A.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Pollard, Nancy S.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    A Framework for Optimal Grasp Contact Planning2017In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, p. 704-711Article in journal (Refereed)
    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.

  • 9.
    Hang, Kaiyu
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Stork, Johannes Andreas
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Pokorny, Florian T.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Combinatorial optimization for hierarchical contact-level grasping2014In: Proceedings - IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2014, p. 381-388Conference paper (Refereed)
    Abstract [en]

    We address the problem of generating force-closed point contact grasps on complex surfaces and model it as a combinatorial optimization problem. Using a multilevel refinement metaheuristic, we maximize the quality of a grasp subject to a reachability constraint by recursively forming a hierarchy of increasingly coarser optimization problems. A grasp is initialized at the top of the hierarchy and then locally refined until convergence at each level. Our approach efficiently addresses the high dimensional problem of synthesizing stable point contact grasps while resulting in stable grasps from arbitrary initial configurations. Compared to a sampling-based approach, our method yields grasps with higher grasp quality. Empirical results are presented for a set of different objects. We investigate the number of levels in the hierarchy, the computational complexity, and the performance relative to a random sampling baseline approach.

  • 10.
    Haustein, Joshua A.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Cruciani, Silvia
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Asif, Rizwan
    KTH.
    Hang, Kaiyu
    KTH.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Placing Objects with prior In-Hand Manipulation using Dexterous Manipulation Graphs2019Conference 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.

  • 11.
    Haustein, Joshua Alexander
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Arnekvist, Isac
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Stork, Johannes A.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Hang, Kaiyu
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Non-prehensile Rearrangement Planning with Learned Manipulation States and Actions2018In: Workshop on "Machine Learning in Robot Motion Planning" at the International Conference on Intelligent Robots and Systems (IROS) 2018, 2018Conference paper (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.

  • 12.
    Haustein, Joshua Alexander
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Hang, Kaiyu
    Department of Mechanical Engineering and Material Science, Yale University.
    Stork, Johannes A.
    Center for Applied Autonomous Sensor Systems (AASS), Örebro University.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Object Placement Planning and Optimization for Robot Manipulators2019In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), 2019Conference 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.

  • 13. Li, Miao
    et al.
    Hang, Kaiyu
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Billard, Aude
    Dexterous grasping under shape uncertainty2016In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 75, p. 352-364Article in journal (Refereed)
    Abstract [en]

    An important challenge in robotics is to achieve robust performance in object grasping and manipulation, dealing with noise and uncertainty. This paper presents an approach for addressing the performance of dexterous grasping under shape uncertainty. In our approach, the uncertainty in object shape is parametrized and incorporated as a constraint into grasp planning. The proposed approach is used to plan feasible hand configurations for realizing planned contacts using different robotic hands. A compliant finger closing scheme is devised by exploiting both the object shape uncertainty and tactile sensing at fingertips. Experimental evaluation demonstrates that our method improves the performance of dexterous grasping under shape uncertainty.

  • 14.
    Madry, Marianna
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Ek, Carl Henrik
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Detry, Renaud
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Hang, Kaiyu
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Improving Generalization for 3D Object Categorization with Global Structure Histograms2012In: Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, IEEE conference proceedings, 2012, p. 1379-1386Conference paper (Refereed)
    Abstract [en]

    We propose a new object descriptor for three dimensional data named the Global Structure Histogram (GSH). The GSH encodes the structure of a local feature response on a coarse global scale, providing a beneficial trade-off between generalization and discrimination. Encoding the structural characteristics of an object allows us to retain low local variations while keeping the benefit of global representativeness. In an extensive experimental evaluation, we applied the framework to category-based object classification in realistic scenarios. We show results obtained by combining the GSH with several different local shape representations, and we demonstrate significant improvements to other state-of-the-art global descriptors.

  • 15.
    Pokorny, Florian T.
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Hang, Kaiyu
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Grasp Moduli Spaces2013In: Proceedings of Robotics: Science and Systems (RSS 2013), 2013Conference paper (Refereed)
    Abstract [en]

    We present a new approach for modelling grasping using an integrated space of grasps and shapes. In particular, we introduce an infinite dimensional space, the Grasp Moduli Space, which represents shapes and grasps in a continuous manner. We define a metric on this space allowing us to formalize ‘nearby’ grasp/shape configurations and we discuss continuous deformations of such configurations. We work in particular with surfaces with cylindrical coordinates and analyse the stability of a popular L1 grasp quality measure Ql under continuous deformations of shapes and grasps. We experimentally determine bounds on the maximal change of Ql in a small neighbourhood around stable grasps with grasp quality above a threshold. In the case of surfaces of revolution, we determine stable grasps which correspond to grasps used by humans and develop an efficient algorithm for generating those grasps in the case of three contact points. We show that sufficiently stable grasps stay stable under small deformations. For larger deformations, we develop a gradient-based method that can transfer stable grasps between different surfaces. Additionally, we show in experiments that our gradient method can be used to find stable grasps on arbitrary surfaces with cylindrical coordinates by deforming such surfaces towards a corresponding ‘canonical’ surface of revolution.

1 - 15 of 15
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