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  • 1.
    Berglund, Erik
    et al.
    Örebro University, School of Science and Technology.
    Iliev, Boyko
    Örebro University, School of Science and Technology.
    Palm, Rainer
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Charusta, Krzysztof
    Örebro University, School of Science and Technology.
    Dimitrov, Dimitar
    Örebro University, School of Science and Technology.
    Mapping between different kinematic structures without absolute positioning during operation2012In: Electronics Letters, ISSN 0013-5194, E-ISSN 1350-911X, Vol. 48, no 18, p. 1110-1112Article in journal (Refereed)
    Abstract [en]

    When creating datasets for modelling of human skills based on training examples from human motion, one can encounter the problem that the kinematics of the robot does not match the human kinematics. Presented is a simple method of bypassing the explicit modelling of the human kinematics based on a variant of the self-organising map (SOM) algorithm. While the literature contains instances of SOM-type algorithms used for dimension reduction, this reported work deals with the inverse problem: dimension increase, as we are going from 4 to 5 degrees of freedom.

  • 2.
    Bunz, Elsa
    et al.
    Örebro University, Örebro, Sweden.
    Chadalavada, Ravi Teja
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Schindler, Maike
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Spatial Augmented Reality and Eye Tracking for Evaluating Human Robot Interaction2016In: Proceedings of RO-MAN 2016 Workshop: Workshop on Communicating Intentions in Human-Robot Interaction, 2016Conference paper (Refereed)
    Abstract [en]

    Freely moving autonomous mobile robots may leadto anxiety when operating in workspaces shared with humans.Previous works have given evidence that communicating in-tentions using Spatial Augmented Reality (SAR) in the sharedworkspace will make humans more comfortable in the vicinity ofrobots. In this work, we conducted experiments with the robotprojecting various patterns in order to convey its movementintentions during encounters with humans. In these experiments,the trajectories of both humans and robot were recorded witha laser scanner. Human test subjects were also equipped withan eye tracker. We analyzed the eye gaze patterns and thelaser scan tracking data in order to understand how the robot’sintention communication affects the human movement behavior.Furthermore, we used retrospective recall interviews to aid inidentifying the reasons that lead to behavior changes.

  • 3.
    Chadalavada, Ravi Teja
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Empirical evaluation of human trust in an expressive mobile robot2016In: Proceedings of RSS Workshop "Social Trust in Autonomous Robots 2016", 2016Conference paper (Refereed)
    Abstract [en]

    A mobile robot communicating its intentions using Spatial Augmented Reality (SAR) on the shared floor space makes humans feel safer and more comfortable around the robot. Our previous work [1] and several other works established this fact. We built upon that work by adding an adaptable information and control to the SAR module. An empirical study about how a mobile robot builds trust in humans by communicating its intentions was conducted. A novel way of evaluating that trust is presented and experimentally shown that adaption in SAR module lead to natural interaction and the new evaluation system helped us discover that the comfort levels between human-robot interactions approached those of human-human interactions.

  • 4.
    Chadalavada, Ravi Teja
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    That’s on my Mind!: Robot to Human Intention Communication through on-board Projection on Shared Floor Space2015In: 2015 European Conference on Mobile Robots (ECMR), New York: IEEE conference proceedings , 2015Conference paper (Refereed)
    Abstract [en]

    The upcoming new generation of autonomous vehicles for transporting materials in industrial environments will be more versatile, flexible and efficient than traditional AGVs, which simply follow pre-defined paths. However, freely navigating vehicles can appear unpredictable to human workers and thus cause stress and render joint use of the available space inefficient. Here we address this issue and propose on-board intention projection on the shared floor space for communication from robot to human. We present a research prototype of a robotic fork-lift equipped with a LED projector to visualize internal state information and intents. We describe the projector system and discuss calibration issues. The robot’s ability to communicate its intentions is evaluated in realistic situations where test subjects meet the robotic forklift. The results show that already adding simple information, such as the trajectory and the space to be occupied by the robot in the near future, is able to effectively improve human response to the robot.

  • 5.
    Charusta, Krzysztof
    et al.
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Dimitrov, Dimitar
    Örebro University, School of Science and Technology.
    Iliev, Boyko
    Örebro University, School of Science and Technology.
    Independent contact regions based on a patch contact model2012In: 2012 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2012, p. 4162-4169Conference paper (Refereed)
    Abstract [en]

    The synthesis of multi-fingered grasps on nontrivial objects requires a realistic representation of the contact between the fingers of a robotic hand and an object. In this work, we use a patch contact model to approximate the contact between a rigid object and a deformable anthropomorphic finger. This contact model is utilized in the computation of Independent Contact Regions (ICRs) that have been proposed as a way to compensate for shortcomings in the finger positioning accuracy of robotic grasping devices. We extend the ICR algorithm to account for the patch contact model and show the benefits of this solution.

  • 6.
    Charusta, Krzysztof
    et al.
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Dimitrov, Dimitar
    Örebro University, School of Science and Technology.
    Iliev, Boyko
    Örebro University, School of Science and Technology.
    Generation of independent contact regions on objects reconstructed from noisy real-world range data2012In: 2012 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2012, p. 1338-1344Conference paper (Refereed)
    Abstract [en]

    The synthesis and evaluation of multi-fingered grasps on complex objects is a challenging problem that has received much attention in the robotics community. Although several promising approaches have been developed, applications to real-world systems are limited to simple objects or gripper configurations. The paradigm of Independent Contact Regions (ICRs) has been proposed as a way to increase the tolerance to grasp positioning errors. This concept is well established, though only on precise geometric object models. This work is concerned with the application of the ICR paradigm to models reconstructed from real-world range data. We propose a method for increasing the robustness of grasp synthesis on uncertain geometric models. The sensitivity of the ICR algorithm to noisy data is evaluated and a filtering approach is proposed to improve the quality of the final result.

  • 7.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Optimization-based robot grasp synthesis and motion control2014Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis investigates the questions of where to grasp and how to grasp a given object with an articulated robotic grasping device. To this end, aspects of grasp synthesis and hand motion planning and control are investigated. Grasp synthesis is the process of determining a palm pose with respect to the target object, as well as a hand joint configuration and/or grasp contact points such that a successful grasp execution is allowed. Existing methods tackling the grasp synthesis problem can be categorized in analytical and empirical approaches. Analytical approaches are based on geometric, kinematic and/or dynamic formulations, whereas empirical methods aim at mimicking human strategies.An overarching idea throughout this thesis is to circumvent the curse of dimensionality, which is inherent in high-dimensional planning problems, by incorporating empirical data in analytical approaches. To this end, tools from the field of constrained optimization are used (i) to synthesize grasp families based on available prototype grasps, (ii) to incorporate heuristics capturing human grasp strategies in the grasp synthesis process and (iii) to encode demonstrated grasp motions in primitive motion controllers.The first contribution is related to the computation and analysis of grasp families which are represented as Independent Contact Regions (ICR) on a target object’s surface. To this end, the well-known concept of the Grasp Wrench Space for a single grasp is extended to be applicable for a set of grasps. Applications of ICR include grasp qualification by capturing the robustness of a grasp to position inaccuracies and the visual guidance of a demonstrator in a teleoperating scenario. In the second main contribution of this thesis, it is shown how to reduce the grasp solution space during the synthesis process by accounting for human approach strategies. This is achieved by imposing appropriate constraints to a corresponding optimization problem. A third contribution in this dissertation is made to reactive motion planning. Here, primitive controllers are synthesized by estimating the free parameters of corresponding dynamical systems from multiple demonstrated trajectories. The approach is evaluated on an anthropomorphic robot hand/arm platform. Also, an extension to a Model Predictive Control (MPC) scheme is presented which allows to incorporate state constraints for auxiliary tasks such as obstacle avoidance.

  • 8.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Dimitar, Dimitrov
    Institut National de Recherche en Informatique et Automatique (INRIA) Rhone Alpes, Grenoble, France.
    Representing movement primitives as implicit dynamical systems learned from multiple demonstrations2013In: Proceedings of the International Conference on Advanced Robotics (ICAR), IEEE, 2013, p. 1-8Conference paper (Refereed)
    Abstract [en]

    This work deals with the problem of parameter estimation of dynamical systems intended to model demonstrated motion profiles for a system of interest. The regression problem is formulated as a constrained nonlinear least squares problem. We present an approach that extends the concept of dynamical movement primitives to account for multiple demonstrations of a motion. We maintain an implicit dynamical system that resembles the demonstrated trajectories in a locally optimal way. This is achieved by solving a quadratic program (that encodes our notion of resemblance) at each sampling time step. Our method guarantees predictable state evolution even in regions of the state space not covered by the demonstrations.

  • 9.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Dimitrov, Dimitar
    INRIA St Ismier, Rhône-Alpes, France .
    Model predictive motion control based on generalized dynamical movement primitives2014In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 77, no 1, p. 17-35Article in journal (Refereed)
    Abstract [en]

    In this work, experimental data is used toestimate the free parameters of dynamical systemsintended to model motion profiles for a robotic system.The corresponding regression problem is formedas a constrained non-linear least squares problem.In our method, motions are generated via embeddedoptimization by combining dynamical movementprimitives in a locally optimal way at each time step.Based on this concept, we introduce a model predictivecontrol scheme which allows generalization overmultiple encoded behaviors depending on the currentposition in the state space, while leveraging the abilityto explicitly account for state constraints to the fulfillmentof additional tasks such as obstacle avoidance.We present a numerical evaluation of our approachand a preliminary verification by generating graspingmotions for the anthropomorphic Shadow Robothand/arm platform.

  • 10.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Dimitrov, Dimitar
    Örebro University, School of Science and Technology.
    Charusta, Krzysztof
    Örebro University, School of Science and Technology.
    Iliev, Boyko
    Örebro University, School of Science and Technology.
    On the efficient computation of independent contact regions for force closure grasps2010In: IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems (IROS 2010), IEEE conference proceedings, 2010, p. 586-591Conference paper (Other academic)
    Abstract [en]

    Since the introduction of independent contact regions in order to compensate for shortcomings in the positioning accuracy of robotic hands, alternative methods for their generation have been proposed. Due to the fact that (in general) such regions are not unique, the computation methods used usually reflect the envisioned application and/or underlying assumptions made. This paper introduces a parallelizable algorithm for the efficient computation of independent contact regions, under the assumption that a user input in the form of initial guess for the grasping points is readily available. The proposed approach works on discretized 3D-objects with any number of contacts and can be used with any of the following models: frictionless point contact, point contact with friction and soft finger contact. An example of the computation of independent contact regions comprising a non-trivial task wrench space is given.

  • 11.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Dimitrov, Dimitar
    Örebro University, School of Science and Technology.
    Charusta, Krzysztof
    Örebro University, School of Science and Technology.
    Iliev, Boyko
    Örebro University, School of Science and Technology.
    Prioritized independent contact regions for form closure grasps2011In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, p. 1797-1803Conference paper (Refereed)
    Abstract [en]

    The concept of independent contact regions on a target object's surface, in order to compensate for shortcomings in the positioning accuracy of robotic grasping devices, is well known. However, the numbers and distributions of contact points forming such regions is not unique and depends on the underlying computational method. In this work we present a computation scheme allowing to prioritize contact points for inclusion in the independent regions. This enables a user to affect their shape in order to meet the demands of the targeted application. The introduced method utilizes frictionless contact constraints and is able to efficiently approximate the space of disturbances resistible by all grasps comprising contacts within the independent regions.

  • 12.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Kragic, Danica
    Centre for Autonomous Systems, Computer Vision and Active Perception Lab, CSC, KTH Stockholm, Stockholm, Sweden.
    Bekiroglu, Yasemin
    School of Mechanical Engineering, University of Birmingham, Birmingham, United Kingdom.
    Analytic Grasp Success Prediction with Tactile Feedback2016In: 2016 IEEE International Conference on Robotics and Automation, ICRA 2016, New York, USA: IEEE , 2016, p. 165-171Conference paper (Refereed)
    Abstract [en]

    Predicting grasp success is useful for avoiding failures in many robotic applications. Based on reasoning in wrench space, we address the question of how well analytic grasp success prediction works if tactile feedback is incorporated. Tactile information can alleviate contact placement uncertainties and facilitates contact modeling. We introduce a wrench-based classifier and evaluate it on a large set of real grasps. The key finding of this work is that exploiting tactile information allows wrench-based reasoning to perform on a level with existing methods based on learning or simulation. Different from these methods, the suggested approach has no need for training data, requires little modeling effort and is computationally efficient. Furthermore, our method affords task generalization by considering the capabilities of the grasping device and expected disturbance forces/moments in a physically meaningful way.

  • 13.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Bonilla, Manuel
    Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy.
    Tincani, Vinicio
    Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy.
    Vaskevicius, Narunas
    Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy.
    Fantoni, Gualtiero
    Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy.
    Birk, Andreas
    Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Bicchi, Antonio
    Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy.
    Improving Grasp Robustness via In-Hand Manipulation with Active Surfaces2014In: Workshop on Autonomous Grasping and Manipulation: An Open Challenge, 2014Conference paper (Refereed)
  • 14.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Bonilla, Manuel
    Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy.
    Tincani, Vinicio
    Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy.
    Vaskevicius, Narunas
    Robotics Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany.
    Fantoni, Gualtiero
    Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy.
    Birk, Andreas
    Robotics Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Bicchi, Antonio
    Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy.
    Velvet fingers: grasp planning and execution for an underactuated gripper with active surfaces2014In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2014, p. 3669-3675Conference paper (Refereed)
    Abstract [en]

    In this work we tackle the problem of planning grasps for an underactuated gripper which enable it to retrieve target objects from a cluttered environment. Furthermore,we investigate how additional manipulation capabilities of the gripping device, provided by active surfaces on the inside of the fingers, can lead to performance improvement in the grasp execution process. To this end, we employ a simple strategy, in which the target object is ‘pulled-in’ towards the palm during grasping which results in firm enveloping grasps. We show the effectiveness of the suggested methods by means of experiments conducted in a real-world scenario.

  • 15.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Grasp Envelopes for Constraint-based Robot Motion Planning and Control2015In: Robotics: Science and Systems Conference: Workshop on Bridging the Gap between Data-driven and Analytical Physics-based Grasping and Manipulation, 2015Conference paper (Refereed)
    Abstract [en]

    We suggest a grasp represen-tation in form of a set of enveloping spatial constraints. Our representation transforms the grasp synthesisproblem (i. e., the question of where to position the graspingdevice) from finding a suitable discrete manipulator wrist pose to finding a suitable pose manifold. Also the correspondingmotion planning and execution problem is relaxed – insteadof transitioning the wrist to a discrete pose, it is enough tomove it anywhere within the grasp envelope which allows toexploit kinematic redundancy.

  • 16.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Tincani, Vinicio
    Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Mosberger, Rafael
    Örebro University, School of Science and Technology.
    Fantoni, Gualtiero
    Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy.
    Bicchi, Antonio
    Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    On Using Optimization-based Control instead of Path-Planning for Robot Grasp Motion Generation2015In: IEEE International Conference on Robotics and Automation (ICRA) - Workshop on Robotic Hands, Grasping, and Manipulation, 2015Conference paper (Refereed)
  • 17.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Tincani, Vinicio
    University of Pisa, Pisa, Italy.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Mosberger, Rafael
    Örebro University, School of Science and Technology.
    Fantoni, Gualtiero
    University of Pisa, Pisa, Italy.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    The Next Step in Robot Commissioning: Autonomous Picking and Palletizing2016In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 1, no 1, p. 546-553Article in journal (Refereed)
    Abstract [en]

    So far, autonomous order picking (commissioning) systems have not been able to meet the stringent demands regarding speed, safety, and accuracy of real-world warehouse automation, resulting in reliance on human workers. In this letter, we target the next step in autonomous robot commissioning: automatizing the currently manual order picking procedure. To this end, we investigate the use case of autonomous picking and palletizing with a dedicated research platform and discuss lessons learned during testing in simplified warehouse settings. The main theoretical contribution is a novel grasp representation scheme which allows for redundancy in the gripper pose placement. This redundancy is exploited by a local, prioritized kinematic controller which generates reactive manipulator motions on-the-fly. We validated our grasping approach by means of a large set of experiments, which yielded an average grasp acquisition time of 23.5 s at a success rate of 94.7%. Our system is able to autonomously carry out simple order picking tasks in a humansafe manner, and as such serves as an initial step toward future commercial-scale in-house logistics automation solutions.

  • 18.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Muthusamy, Rajkumar
    Aalto University, Esbo, Finland.
    Kyrki, Ville
    Aalto University, Esbo, Finland.
    Grasp Envelopes: Extracting Constraints on Gripper Postures from Online Reconstructed 3D Models2016In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), New York: Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 885-892Conference paper (Refereed)
    Abstract [en]

    Grasping systems that build upon meticulously planned hand postures rely on precise knowledge of object geometry, mass and frictional properties - assumptions which are often violated in practice. In this work, we propose an alternative solution to the problem of grasp acquisition in simple autonomous pick and place scenarios, by utilizing the concept of grasp envelopes: sets of constraints on gripper postures. We propose a fast method for extracting grasp envelopes for objects that fit within a known shape category, placed in an unknown environment. Our approach is based on grasp envelope primitives, which encode knowledge of human grasping strategies. We use environment models, reconstructed from noisy sensor observations, to refine the grasp envelope primitives and extract bounded envelopes of collision-free gripper postures. Also, we evaluate the envelope extraction procedure both in a stand alone fashion, as well as an integrated component of an autonomous picking system.

  • 19.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Vaskevicius, Narunas
    Jacobs University Bremen, Bremen, Germany.
    Mueller, Christian Atanas
    Jacobs University Bremen, Bremen, Germany.
    Fromm, Tobias
    Jacobs University Bremen, Bremen, Germany.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Tincani, Vinicio
    University of Pisa, Pisa, Italy.
    Mojtahedzadeh, Rasoul
    Örebro University, School of Science and Technology.
    Kunaschk, Stefan
    Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany.
    Ernits, R. Mortensen
    Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany.
    Canelhas, Daniel R.
    Örebro University, School of Science and Technology.
    Bonilla, Manuell
    University of Pisa, Pisa, Italy.
    Schwertfeger, Soeren
    ShanghaiTech University, Shanghai, China.
    Bonini, Marco
    Reutlingen University, Reutlingen, Germany.
    Halfar, Harry
    Reutlingen University, Reutlingen, Germany.
    Pathak, Kaustubh
    Jacobs University Bremen, Bremen, Germany.
    Rohde, Moritz
    Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany.
    Fantoni, Gualtiero
    University of Pisa, Pisa, Italy.
    Bicchi, Antonio
    Università di Pisa & Istituto Italiano di Tecnologia, Genova, Italy.
    Birk, Andreas
    Jacobs University, Bremen, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Echelmeyer, Wolfgang
    Reutlingen University, Reutlingen, Germany.
    No More Heavy Lifting: Robotic Solutions to the Container-Unloading Problem2016In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 23, no 4, p. 94-106Article in journal (Refereed)
  • 20.
    Tincani, Vinicio
    et al.
    University of Pisa, Pisa, Italy.
    Catalano, Manuel
    University of Pisa, Pisa, Italy.
    Grioli, Giorgio
    University of Pisa, Pisa, Italy.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Fantoni, Gualtiero
    University of Pisa, Pisa, Italy.
    Bicchi, Antonio
    University of Pisa, Pisa, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy.
    Sensitive Active Surfaces on the Velvet II Dexterous Gripper2015Conference paper (Refereed)
  • 21.
    Tincani, Vinicio
    et al.
    University of Pisa, Pisa, Italy.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Catalano, Manuel
    University of Pisa, Pisa, Italy.
    Grioli, Giorgio
    University of Pisa, Pisa, Italy.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Fantoni, Gualtiero
    University of Pisa, Pisa, Italy.
    Bicchi, Antonio
    Istituto Italiano di Tecnologia, Genova, Italy.
    The Grasp Acquisition Strategy of the Velvet II2015Conference paper (Refereed)
1 - 21 of 21
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