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  • 101.
    Hang, Kaiyu
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Li, Miao
    EPFL.
    Stork, Johannes A.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Bekiroglu, Yasemin
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Pokorny, Florian T.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Billard, Aude
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Hierarchical Fingertip Space: A Unified Framework for Grasp Planning and In-Hand Grasp Adaptation2016Inngår i: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 32, nr 4, s. 960-972, artikkel-id 7530865Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 102.
    Hang, Kaiyu
    et al.
    Yale Univ, Dept Mech Engn & Mat Sci, New Haven, CT 06520 USA..
    Lyu, Ximin
    Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China..
    Song, Haoran
    Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China..
    Stork, Johannes A.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL. Örebro Univ, Ctr Appl Autonomous Sensor Syst AASS, Örebro, Sweden.
    Dollar, Aaron M.
    Yale Univ, Dept Mech Engn & Mat Sci, New Haven, CT 06520 USA..
    Kragic, Danica
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Zhang, Fu
    Univ Hong Kong, Hong Kong, Peoples R China..
    Perching and resting-A paradigm for UAV maneuvering with modularized landing gears2019Inngår i: SCIENCE ROBOTICS, ISSN 2470-9476, Vol. 4, nr 28, artikkel-id eaau6637Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Perching helps small unmanned aerial vehicles (UAVs) extend their time of operation by saving battery power. However, most strategies for UAV perching require complex maneuvering and rely on specific structures, such as rough walls for attaching or tree branches for grasping. Many strategies to perching neglect the UAV's mission such that saving battery power interrupts the mission. We suggest enabling UAVs with the capability of making and stabilizing contacts with the environment, which will allow the UAV to consume less energy while retaining its altitude, in addition to the perching capability that has been proposed before. This new capability is termed "resting." For this, we propose a modularized and actuated landing gear framework that allows stabilizing the UAV on a wide range of different structures by perching and resting. Modularization allows our framework to adapt to specific structures for resting through rapid prototyping with additive manufacturing. Actuation allows switching between different modes of perching and resting during flight and additionally enables perching by grasping. Our results show that this framework can be used to perform UAV perching and resting on a set of common structures, such as street lights and edges or corners of buildings. We show that the design is effective in reducing power consumption, promotes increased pose stability, and preserves large vision ranges while perching or resting at heights. In addition, we discuss the potential applications facilitated by our design, as well as the potential issues to be addressed for deployment in practice.

  • 103.
    Hang, Kaiyu
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Pokorny, Florian T.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Friction Coefficients and Grasp Synthesis2013Inngår i: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), IEEE , 2013, s. 3520-3526Konferansepaper (Fagfellevurdert)
    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.

  • 104.
    Hang, Kaiyu
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Stork, Johannes A.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Hierarchical Fingertip Space for Multi-fingered Precision Grasping2014Inngår i: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2014), IEEE , 2014, s. 1641-1648Konferansepaper (Fagfellevurdert)
    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.

  • 105.
    Hang, Kaiyu
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.
    Stork, Johannes A.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.
    Pollard, Nancy S.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.
    A Framework for Optimal Grasp Contact Planning2017Inngår i: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, nr 2, s. 704-711Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 106.
    Hang, Kaiyu
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Stork, Johannes Andreas
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Pokorny, Florian T.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Combinatorial optimization for hierarchical contact-level grasping2014Inngår i: Proceedings - IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2014, s. 381-388Konferansepaper (Fagfellevurdert)
    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.

  • 107.
    Haustein, Joshua A.
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Cruciani, Silvia
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Asif, Rizwan
    KTH.
    Hang, Kaiyu
    KTH.
    Kragic, Danica
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Placing Objects with prior In-Hand Manipulation using Dexterous Manipulation Graphs2019Konferansepaper (Fagfellevurdert)
    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.

  • 108.
    Haustein, Joshua Alexander
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Arnekvist, Isac
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Stork, Johannes A.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Hang, Kaiyu
    Department of Mechanical Engineering and Material Science, Yale University, New Haven, Connecticut, USA.
    Kragic, Danica
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Learning Manipulation States and Actions for Efficient Non-prehensile Rearrangement PlanningManuskript (preprint) (Annet vitenskapelig)
    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.

  • 109.
    Haustein, Joshua Alexander
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Arnekvist, Isac
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Stork, Johannes A.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Hang, Kaiyu
    Kragic, Danica
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Non-prehensile Rearrangement Planning with Learned Manipulation States and Actions2018Inngår i: Workshop on "Machine Learning in Robot Motion Planning" at the International Conference on Intelligent Robots and Systems (IROS) 2018, 2018Konferansepaper (Fagfellevurdert)
    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.

  • 110.
    Haustein, Joshua Alexander
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, 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, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Object Placement Planning and Optimization for Robot Manipulators2019Inngår i: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), 2019Konferansepaper (Fagfellevurdert)
    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.

  • 111.
    Haustein, Joshua
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Hang, Kaiyu
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Integrating motion and hierarchical fingertip grasp planning2017Inngår i: 2017 IEEE International Conference on Robotics and Automation (ICRA), Institute of Electrical and Electronics Engineers (IEEE), 2017, s. 3439-3446, artikkel-id 7989392Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this work, we present an algorithm that simultaneously searches for a high quality fingertip grasp and a collision-free path for a robot hand-arm system to achieve it. The algorithm combines a bidirectional sampling-based motion planning approach with a hierarchical contact optimization process. Rather than tackling these problems in a decoupled manner, the grasp optimization is guided by the proximity to collision-free configurations explored by the motion planner. We implemented the algorithm for a 13-DoF manipulator and show that it is capable of efficiently planning reachable high quality grasps in cluttered environments. Further, we show that our algorithm outperforms a decoupled integration in terms of planning runtime.

  • 112.
    Hjelm, Martin
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Detry, R.
    Ek, Carl Henrik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Representations for cross-task, cross-object grasp Transfer2014Inngår i: Proceedings - IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2014, s. 5699-5704Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We address The problem of Transferring grasp knowledge across objects and Tasks. This means dealing with Two important issues: 1) The induction of possible Transfers, i.e., whether a given object affords a given Task, and 2) The planning of a grasp That will allow The robot To fulfill The Task. The induction of object affordances is approached by abstracting The sensory input of an object as a set of attributes That The agent can reason about Through similarity and proximity. For grasp execution, we combine a part-based grasp planner with a model of Task constraints. The Task constraint model indicates areas of The object That The robot can grasp To execute The Task. Within These areas, The part-based planner finds a hand placement That is compatible with The object shape. The key contribution is The ability To Transfer Task parameters across objects while The part-based grasp planner allows for Transferring grasp information across Tasks. As a result, The robot is able To synthesize plans for previously unobserved Task/object combinations. We illustrate our approach with experiments conducted on a real robot.

  • 113.
    Hjelm, Martin
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Ek, Carl Henrik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Detry, Renaud
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kjellström, Hedvig
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Sparse Summarization of Robotic Grasping Data2013Inngår i: 2013 IEEE International Conference on Robotics and Automation (ICRA), New York: IEEE , 2013, s. 1082-1087Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We propose a new approach for learning a summarized representation of high dimensional continuous data. Our technique consists of a Bayesian non-parametric model capable of encoding high-dimensional data from complex distributions using a sparse summarization. Specifically, the method marries techniques from probabilistic dimensionality reduction and clustering. We apply the model to learn efficient representations of grasping data for two robotic scenarios.

  • 114.
    Hjelm, Martin
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Ek, Carl Henrik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Detry, Renaud
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Learning Human Priors for Task-Constrained Grasping2015Inngår i: COMPUTER VISION SYSTEMS (ICVS 2015), Springer Berlin/Heidelberg, 2015, s. 207-217Konferansepaper (Fagfellevurdert)
    Abstract [en]

    An autonomous agent using manmade objects must understand how task conditions the grasp placement. In this paper we formulate task based robotic grasping as a feature learning problem. Using a human demonstrator to provide examples of grasps associated with a specific task, we learn a representation, such that similarity in task is reflected by similarity in feature. The learned representation discards parts of the sensory input that is redundant for the task, allowing the agent to ground and reason about the relevant features for the task. Synthesized grasps for an observed task on previously unseen objects can then be filtered and ordered by matching to learned instances without the need of an analytically formulated metric. We show on a real robot how our approach is able to utilize the learned representation to synthesize and perform valid task specific grasps on novel objects.

  • 115.
    Hyttinen, Emil
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Detry, R.
    Learning the tactile signatures of prototypical object parts for robust part-based grasping of novel objects2015Inngår i: Proceedings - IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2015, nr June, s. 4927-4932Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We present a robotic agent that learns to derive object grasp stability from touch. The main contribution of our work is the use of a characterization of the shape of the part of the object that is enclosed by the gripper to condition the tactile-based stability model. As a result, the agent is able to express that a specific tactile signature may for instance indicate stability when grasping a cylinder, while cuing instability when grasping a box. We proceed by (1) discretizing the space of graspable object parts into a small set of prototypical shapes, via a data-driven clustering process, and (2) learning a touch-based stability classifier for each prototype. Classification is conducted through kernel logistic regression, applied to a low-dimensional approximation of the tactile data read from the robot's hand. We present an experiment that demonstrates the applicability of the method, yielding a success rate of 89%. Our experiment also shows that the distribution of tactile data differs substantially between grasps collected with different prototypes, supporting the use of shape cues in touch-based stability estimators.

  • 116.
    Hyttinen, Emil
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Detry, Renaud
    Estimating Tactile Data for Adaptive Grasping of Novel Objects2017Inngår i: 2017 IEEE-RAS 17TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTICS (HUMANOIDS), Institute of Electrical and Electronics Engineers (IEEE), 2017, s. 643-648Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We present an adaptive grasping method that finds stable grasps on novel objects. The main contributions of this paper is in the computation of the probability of success of grasps in the vicinity of an already applied grasp. Our method performs adaptions by simulating tactile data for grasps in the vicinity of the current grasp. The simulated data is used to evaluate hypothetical configurations and thereby guide the robot in the right direction. We demonstrate the applicability of our method by constructing a system that can plan, apply and adapt grasps on novel objects. Experiments are conducted on objects from the YCB object set, [1], and our method increases the robot's success rate from 71.4% to 88.1%. Our experiments show that the application of our grasp adaption method improves grasp stability significantly.

  • 117.
    Högman, Virgile
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Björkman, Mårten
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Interactive object classification using sensorimotor contingencies2013Inngår i: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE , 2013, s. 2799-2805Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Understanding and representing objects and their function is a challenging task. Objects we manipulate in our daily activities can be described and categorized in various ways according to their properties or affordances, depending also on our perception of those. In this work, we are interested in representing the knowledge acquired through interaction with objects, describing these in terms of action-effect relations, i.e. sensorimotor contingencies, rather than static shape or appearance representations. We demonstrate how a robot learns sensorimotor contingencies through pushing using a probabilistic model. We show how functional categories can be discovered and how entropy-based action selection can improve object classification.

  • 118.
    Högman, Virgile
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Björkman, Mårten
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Maki, Atsuto
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    A sensorimotor learning framework for object categorization2016Inngår i: IEEE Transactions on Cognitive and Developmental Systems, ISSN 2379-8920, Vol. 8, nr 1, s. 15-25Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a framework that enables a robot to discover various object categories through interaction. The categories are described using action-effect relations, i.e. sensorimotor contingencies rather than more static shape or appearance representation. The framework provides a functionality to classify objects and the resulting categories, associating a class with a specific module. We demonstrate the performance of the framework by studying a pushing behavior in robots, encoding the sensorimotor contingencies and their predictability with Gaussian Processes. We show how entropy-based action selection can improve object classification and how functional categories emerge from the similarities of effects observed among the objects. We also show how a multidimensional action space can be realized by parameterizing pushing using both position and velocity.

  • 119.
    Hübner, Kai
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Björkman, Mårten
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Rasolzadeh, Babak
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Schmidt, Martina
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Integration of visual and shape attributes for object action complexes2008Inngår i: Computer Vision Systems, Proceedings / [ed] Gasteratos, A; Vincze, M; Tsotsos, JK, 2008, Vol. 5008, s. 13-22Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Our work is oriented towards the idea of developing cognitive capabilities in artificial systems through Object Action Complexes (OACs) [7]. The theory comes up with the claim that objects and actions are inseparably intertwined. Categories of objects are not built by visual appearance only, as very common in computer vision, but by the actions an agent can perform and by attributes perceivable. The core of the OAC concept is constituting objects from a set of attributes, which can be manifold in type (e.g. color, shape, mass, material), to actions. This twofold of attributes and actions provides the base for categories. The work presented here is embedded in the development of an extensible system for providing and evolving attributes,, beginning with attributes extractable from visual data.

  • 120.
    Hübner, Kai
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Grasping by parts: Robot grasp generation from 3D box primitives2010Inngår i: 4th International Conference on Cognitive Systems, CogSys 2010, 2010Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Robot grasping capabilities are essential for perceiving, interpreting and acting in arbitrary and dynamic environments. While classical computer vision and visual interpretation of scenes focus on the robot's internal representation of the world rather passively, robot grasping capabilities are needed to actively execute tasks, modify scenarios and thereby reach versatile goals. Grasping is a central issue of various robot applications, especially when unknown objects have to be manipulated by the system. We present an approach aimed at the object description, but constrain it by performable actions. In particular, we will connect box-like representations of objects with grasping, and motivate this approach in a number of ways. The contributions of our work are two-fold: in terms of shape approximation, we provide an algorithm for a 3D box primitive representation to identify object parts from 3D point clouds. We motivate and evaluate this choice particularly toward the task of grasping. As a contribution in the field of grasping, we present a grasp hypothesis generation framework that utilizes the box presentation in a highly flexible manner.

  • 121.
    Hübner, Kai
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Selection of Robot Pre-Grasps using Box-Based Shape Approximation2008Inngår i: 2008 IEEE/RSJ International Conference On Robots And Intelligent Systems, Vols 1-3, Conference Proceedings / [ed] Chatila, R; Kelly, A; Merlet, JP, 2008, s. 1765-1770Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Grasping is a central issue of various robot applications, especially when unknown objects have to be manipulated by the system. In earlier work, we have shown the efficiency of 3D object shape approximation by box primitives for the purpose of grasping. A point cloud was approximated by box primitives [1]. In this paper, we present a continuation of these ideas and focus on the box representation itself. On the number of grasp hypotheses from box face normals, we apply heuristic selection integrating task, orientation and shape issues. Finally, an off-line trained neural network is applied to chose a final best hypothesis as the final grasp. We motivate how boxes as one of the simplest representations can be applied in a more sophisticated manner to generate task-dependent grasps.

  • 122.
    Hübner, Kai
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Ruthotto, Steffen
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Minimum Volume Bounding Box decomposition for shape approximation in robot grasping2008Inngår i: 2008 IEEE International Conference on Robotics and Automation, ICRA 2008: Vols 1-9, 2008, s. 1628-1633Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Thinking about intelligent robots involves consideration of how such systems can be enabled to perceive, interpret and act in arbitrary and dynamic environments. While sensor perception and model interpretation focus on the robot's internal representation of the world rather passively, robot grasping capabilities are needed to actively execute tasks, modify scenarios and thereby reach versatile goals. These capabilities should also include the generation of stable grasps to safely handle even objects unknown to the robot. We believe that the key to this ability is not to select a good grasp depending on the identification of an object (e.g. as a cup), but on its shape (e.g. as a composition of shape primitives). In this paper, we envelop given 3D data points into primitive box shapes by a fit-and-split algorithm that is based on an efficient Minimum Volume Bounding Box implementation. Though box shapes are not able to approximate arbitrary data in a precise manner, they give efficient clues for planning grasps on arbitrary objects. We present the algorithm and experiments using the 3D grasping simulator GraspIt! [1].

  • 123.
    Hübner, Kai
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Welke, Kai
    Przybylski, Markus
    Vahrenkamp, Nikolaus
    Asfour, Tamim
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Dillmann, Rudiger
    Grasping Known Objects with Humanoid Robots: A Box-Based Approach2009Inngår i: 2009 International Conference on Advanced Robotics, ICAR 2009, IEEE , 2009, s. 179-184Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Autonomous grasping of household objects is one of the major skills that an intelligent service robot necessarily has to provide in order to interact with the environment. In this paper, we propose a grasping strategy for known objects, comprising an off-line, box-based grasp generation technique on 3D shape representations. The complete system is able to robustly detect an object and estimate its pose, flexibly generate grasp hypotheses from the assigned model and perform such hypotheses using visual servoing. We will present experiments implemented on the humanoid platform ARMAR-III.

  • 124.
    Jensfelt, Patric
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Ekvall, Staffan
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Integrating SLAM and Object Detection for Service Robot Tasks2005Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    A mobile robot system operating in a domestic environment has to integrate components from a number of key research areas such as recognition, visual tracking, visual servoing, object grasping, robot localization, etc. There also has to be an underlying methodology to facilitate the integration. We have previously showed that through sequencing of basic skills, provided by the above mentioned competencies, the system has the ability to carry out flexible grasping for fetch and carry tasks in realistic environments. Through careful fusion of reactive and deliberative control and use of multiple sensory modalities a flexible system is achieved. However, our previous work has mostly concentrated on pick-and-place tasks leaving limited place for generalization. Currently, we are interested in more complex tasks such as collaborating and helping humans in their everyday tasks, opening doors and cupboards, building maps of the environment including objects that are automatically recognized by the system. In this paper, we will show some of the current results regarding the above. Most systems for simultaneous localization and mapping (SLAM) build maps that are only used for localizing the robot. Such maps are typically based on grids or different types of features such as point and lines. Here we augment the process with an object recognition system that detects objects in the environment and puts them in the map generated by the SLAM system. The metric map is also split into topological entities corresponding to rooms. In this way the user can command the robot to retrieve a certain object from a certain room.

  • 125.
    Jensfelt, Patric
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Ekvall, Staffan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Aarno, Daniel
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Augmenting slam with object detection in a service robot framework2006Inngår i: Proceedings, IEEE International Workshop on Robot and Human Interactive Communication, 2006, s. 741-746Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In a service robot scenario, we are interested in a task of building maps of the environment that include automatically recognized objects. Most systems for simultaneous localization and mapping (SLAM) build maps that are only used for localizing the robot. Such maps are typically based on grids or different types of features such as point and lines. Here, we augment the process with an object recognition system that detects objects in the environment and puts them in the map generated by the SLAM system. During task execution, the robot can use this information to reason about objects, places and their relationships. The metric map is also split into topological entities corresponding to rooms. In this way, the user can command the robot to retrieve an object from a particular room or get help from a robot when searching for a certain object

  • 126.
    Jensfelt, Patric
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Folkesson, John
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Christensen, Henrik I.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Exploiting distinguishable image features in robotic mapping and localization2006Inngår i: European Robotics Symposium 2006 / [ed] Christensen, HI, 2006, Vol. 22, s. 143-157Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Simultaneous localization and mapping (SLAM) is an important research area in robotics. Lately, systems that use a single bearing-only sensors have received significant attention and the use of visual sensors have been strongly advocated. In this paper, we present a framework for 3D bearing only SLAM using a single camera. We concentrate on image feature selection in order to achieve precise localization and thus good reconstruction in 3D. In addition, we demonstrate how these features can be managed to provide real-time performance and fast matching, to detect loop-closing situations. The proposed vision system has been combined with an extended Kalman Filter (EKF) based SLAM method. A number of experiments have been performed in indoor environments which demonstrate the validity and effectiveness of the approach. We also show how the SLAM generated map can be used for robot localization. The use of vision features which are distinguishable allows a straightforward solution to the "kidnapped-robot" scenario.

  • 127.
    Jensfelt, Patric
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Folkesson, John
    Dept of Mechanical Engineering, Massachusetts Institute of Technology.
    Bearing-Only Vision SLAM with Distinguishable Image Feature2007Inngår i: Vision Systems Applications / [ed] Goro Obinata and Ashish Dutta, InTech, 2007Kapittel i bok, del av antologi (Fagfellevurdert)
  • 128.
    Jensfelt, Patric
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Folkesson, John
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Björkman, Mårten
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    A framework for vision based bearing only 3D SLAM2006Inngår i: Proceedings of the 2006 IEEE International Conference on Robotics and Automation, Orlando, Florida - May 2006: Vols 1-10, IEEE , 2006, s. 1944-1950Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a framework for 3D vision based bearing only SLAM using a single camera, an interesting setup for many real applications due to its low cost. The focus in is on the management of the features to achieve real-time performance in extraction, matching and loop detection. For matching image features to map landmarks a modified, rotationally variant SIFT descriptor is used in combination with a Harris-Laplace detector. To reduce the complexity in the map estimation while maintaining matching performance only a few, high quality, image features are used for map landmarks. The rest of the features are used for matching. The framework has been combined with an EKF implementation for SLAM. Experiments performed in indoor environments are presented. These experiments demonstrate the validity and effectiveness of the approach. In particular they show how the robot is able to successfully match current image features to the map when revisiting an area.

  • 129.
    Johnson-Roberson, Matthew
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Bohg, Jeannette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Björkman, Mårten
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Attention-based Active 3D Point Cloud Segmentation2010Inngår i: IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010, s. 1165-1170Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper we present a framework for the segmentation of multiple objects from a 3D point cloud. We extend traditional image segmentation techniques into a full 3D representation. The proposed technique relies on a state-of-the-art min-cut framework to perform a fully 3D global multi-class labeling in a principled manner. Thereby, we extend our previous work in which a single object was actively segmented from the background. We also examine several seeding methods to bootstrap the graphical model-based energy minimization and these methods are compared over challenging scenes. All results are generated on real-world data gathered with an active vision robotic head. We present quantitive results over aggregate sets as well as visual results on specific examples.

  • 130.
    Johnson-Roberson, Matthew
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Bohg, Jeannette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Skantze, Gabriel
    KTH, Skolan för datavetenskap och kommunikation (CSC), Tal, musik och hörsel, TMH, Tal-kommunikation.
    Gustafson, Joakim
    KTH, Skolan för datavetenskap och kommunikation (CSC), Tal, musik och hörsel, TMH, Tal-kommunikation.
    Carlson, Rolf
    KTH, Skolan för datavetenskap och kommunikation (CSC), Tal, musik och hörsel, TMH, Tal-kommunikation.
    Enhanced visual scene understanding through human-robot dialog2010Inngår i: Dialog with Robots: AAAI 2010 Fall Symposium, 2010, s. -144Konferansepaper (Fagfellevurdert)
  • 131.
    Johnson-Roberson, Matthew
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Bohg, Jeannette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Skantze, Gabriel
    KTH, Skolan för datavetenskap och kommunikation (CSC), Tal, musik och hörsel, TMH.
    Gustafsson, Joakim
    KTH, Skolan för datavetenskap och kommunikation (CSC), Tal, musik och hörsel, TMH.
    Carlson, Rolf
    KTH, Skolan för datavetenskap och kommunikation (CSC), Tal, musik och hörsel, TMH.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Rasolzadeh, Babak
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Enhanced Visual Scene Understanding through Human-Robot Dialog2011Inngår i: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE , 2011, s. 3342-3348Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We propose a novel human-robot-interaction framework for robust visual scene understanding. Without any a-priori knowledge about the objects, the task of the robot is to correctly enumerate how many of them are in the scene and segment them from the background. Our approach builds on top of state-of-the-art computer vision methods, generating object hypotheses through segmentation. This process is combined with a natural dialog system, thus including a ‘human in the loop’ where, by exploiting the natural conversation of an advanced dialog system, the robot gains knowledge about ambiguous situations. We present an entropy-based system allowing the robot to detect the poorest object hypotheses and query the user for arbitration. Based on the information obtained from the human-robot dialog, the scene segmentation can be re-seeded and thereby improved. We present experimental results on real data that show an improved segmentation performance compared to segmentation without interaction.

  • 132.
    Karasalo, Maja
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Piccolo, Giacomo
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Hu, Xiaoming
    KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Optimeringslära och systemteori.
    Contour Reconstruction using Recursive Smoothing Splines - Algorithms and Experimental Validation2009Inngår i: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 57, nr 6-7, s. 617-628Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this paper, a recursive smoothing splineapproach for contour reconstruction is studied and evaluated.  Periodic smoothing splines areused by a robot to approximate the contour of encountered obstaclesin the environment.  The splines are generated through minimizing acost function subject to constraints imposed by a linear controlsystem and accuracy is improved iteratively using a recursive splinealgorithm.  The filtering effect of the smoothing splines allows forusage of noisy sensor data and the method is robust with respect to odometrydrift. The algorithm is extensively evaluated in simulationsfor various contours and in experiments using a SICK laser scanner mounted on a PowerBot fromActivMedia Robotics

  • 133.
    Karayiannidis, Yiannis
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Dimarogonas, Dimos
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Multi-agent average consensus control with prescribed performance guarantees2012Inngår i: 2012 IEEE 51st Annual Conference on Decision and Control (CDC), IEEE , 2012, s. 2219-2225Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This work proposes a distributed control scheme for the state agreement problem which can guarantee prescribed performance for the system transient. In particular, i) we consider a set of agents that can exchange information according to a static communication graph, ii) we a priori define time-dependent constraints at the edge's space (errors between agents that exchange information) and iii) we design a distributed controller to guarantee that the errors between the neighboring agents do not violate the constraints. Following this technique the contributions are twofold: a) the convergence rate of the system and the communication structure of the agents' network which are strictly connected can be decoupled, and b) the connectivity properties of the initially formed communication graph are rendered invariant by appropriately designing the prescribed performance bounds. It is also shown how the structure and the parameters of the prescribed performance controller can be chosen in case of connected tree graphs and connected graphs with cycles. Simulation results validate the theoretically proven findings while enlightening the merit of the proposed prescribed performance agreement protocol as compared to the linear one.

  • 134.
    Karayiannidis, Yiannis
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. Chalmers, Sweden.
    Smith, Christian
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Barrientos, Francisco Eli Vina
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Ögren, Petter
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    An Adaptive Control Approach for Opening Doors and Drawers Under Uncertainties2016Inngår i: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 32, nr 1, s. 161-175Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We study the problem of robot interaction with mechanisms that afford one degree of freedom motion, e.g., doors and drawers. We propose a methodology for simultaneous compliant interaction and estimation of constraints imposed by the joint. Our method requires no prior knowledge of the mechanisms' kinematics, including the type of joint, prismatic or revolute. The method consists of a velocity controller that relies on force/torque measurements and estimation of the motion direction, the distance, and the orientation of the rotational axis. It is suitable for velocity controlled manipulators with force/torque sensor capabilities at the end-effector. Forces and torques are regulated within given constraints, while the velocity controller ensures that the end-effector of the robot moves with a task-related desired velocity. We give proof that the estimates converge to the true values under valid assumptions on the grasp, and error bounds for setups with inaccuracies in control, measurements, or modeling. The method is evaluated in different scenarios involving opening a representative set of door and drawer mechanisms found in household environments.

  • 135.
    Karayiannidis, Yiannis
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Smith, Christian
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Mapping Human Intentions to Robot Motions via Physical Interaction Through a Jointly-held Object2014Inngår i: Robot and Human Interactive Communication, 2014 RO-MAN: The 23rd IEEE International Symposium on, 2014, s. 391-397Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper we consider the problem of human-robot collaborative manipulation of an object, where the human is active in controlling the motion, and the robot is passively following the human's lead. Assuming that the human grasp of the object only allows for transfer of forces and not torques, there is a disambiguity as to whether the human desires translation or rotation. In this paper, we analyze different approaches to this problem both theoretically and in experiment. This leads to the proposal of a control methodology that uses switching between two different admittance control modes based on the magnitude of measured force to achieve disambiguation of the rotation/translation problem.

  • 136.
    Karayiannidis, Yiannis
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Smith, Christian
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Vina, Francisco
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Online Contact Point Estimation for Uncalibrated Tool Use2014Inngår i: Robotics and Automation (ICRA), 2014 IEEE International Conference on, IEEE Robotics and Automation Society, 2014, s. 2488-2493Konferansepaper (Fagfellevurdert)
    Abstract [en]

    One of the big challenges for robots working outside of traditional industrial settings is the ability to robustly and flexibly grasp and manipulate tools for various tasks. When a tool is interacting with another object during task execution, several problems arise: a tool can be partially or completely occluded from the robot's view, it can slip or shift in the robot's hand - thus, the robot may lose the information about the exact position of the tool in the hand. Thus, there is a need for online calibration and/or recalibration of the tool. In this paper, we present a model-free online tool-tip calibration method that uses force/torque measurements and an adaptive estimation scheme to estimate the point of contact between a tool and the environment. An adaptive force control component guarantees that interaction forces are limited even before the contact point estimate has converged. We also show how to simultaneously estimate the location and normal direction of the surface being touched by the tool-tip as the contact point is estimated. The stability of the the overall scheme and the convergence of the estimated parameters are theoretically proven and the performance is evaluated in experiments on a real robot.

  • 137.
    Karayiannidis, Yiannis
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Smith, Christian
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Vina, Francisco
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Online Kinematics Estimation for Active Human-Robot Manipulation of Jointly Held Objects2013Inngår i: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE , 2013, s. 4872-4878Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper introduces a method for estimating the constraints imposed by a human agent on a jointly manipulated object. These estimates can be used to infer knowledge of where the human is grasping an object, enabling the robot to plan trajectories for manipulating the object while subject to the constraints. We describe the method in detail, motivate its validity theoretically, and demonstrate its use in co-manipulation tasks with a real robot.

  • 138.
    Karayiannidis, Yiannis
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Smith, Christian
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Vina, Francisco
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Ögren, Petter
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Design of force-driven online motion plans for door opening under uncertainties2012Inngår i: Workshop on Real-time Motion Planning: Online, Reactive, and in Real-time, 2012Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The problem of door opening is fundamental for household robotic applications. Domestic environments are generally less structured than industrial environments and thus several types of uncertainties associated with the dynamics and kinematics of a door must be dealt with to achieve successful opening. This paper proposes a method that can open doors without prior knowledge of the door kinematics. The proposed method can be implemented on a velocity-controlled manipulator with force sensing capabilities at the end-effector. The velocity reference is designed by using feedback of force measurements while constraint and motion directions are updated online based on adaptive estimates of the position of the door hinge. The online estimator is appropriately designed in order to identify the unknown directions. The proposed scheme has theoretically guaranteed performance which is further demonstrated in experiments on a real robot. Experimental results additionally show the robustness of the proposed method under disturbances introduced by the motion of the mobile platform.

  • 139.
    Karayiannidis, Yiannis
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Smith, Christian
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Vina, Francisco
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Ögren, Petter
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Interactive perception and manipulation of unknown constrained mechanisms using adaptive control2013Inngår i: ICRA 2013 Mobile Manipulation Workshop on Interactive Perception, 2013Konferansepaper (Fagfellevurdert)
  • 140.
    Karayiannidis, Yiannis
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Smith, Christian
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Vina, Francisco
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Ögren, Petter
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Model-free robot manipulation of doors and drawers by means of fixed-grasps2013Inngår i: 2013 IEEE International Conference on Robotics and Automation (ICRA), New York: IEEE , 2013, s. 4485-4492Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper addresses the problem of robot interaction with objects attached to the environment through joints such as doors or drawers. We propose a methodology that requires no prior knowledge of the objects’ kinematics, including the type of joint - either prismatic or revolute. The method consists of a velocity controller which relies onforce/torque measurements and estimation of the motion direction,rotational axis and the distance from the center of rotation.The method is suitable for any velocity controlled manipulatorwith a force/torque sensor at the end-effector. The force/torquecontrol regulates the applied forces and torques within givenconstraints, while the velocity controller ensures that the endeffectormoves with a task-related desired tangential velocity. The paper also provides a proof that the estimates converge tothe actual values. The method is evaluated in different scenarios typically met in a household environment.

  • 141.
    Karayiannidis, Yiannis
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Smith, Christian
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Vina, Francisco
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Ögren, Petter
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    "Open Sesame!" Adaptive Force/Velocity Control for Opening Unknown Doors2012Inngår i: Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, IEEE , 2012, s. 4040-4047Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The problem of door opening is fundamental for robots operating in domestic environments. Since these environments are generally less structured than industrial environments, several types of uncertainties associated with the dynamics and kinematics of a door must be dealt with to achieve successful opening. This paper proposes a method that can open doors without prior knowledge of the door kinematics. The proposed method can be implemented on a velocity-controlled manipulator with force sensing capabilities at the end-effector. The method consists of a velocity controller which uses force measurements and estimates of the radial direction based on adaptive estimates of the position of the door hinge. The control action is decomposed into an estimated radial and tangential direction following the concept of hybrid force/motion control. A force controller acting within the velocity controller regulates the radial force to a desired small value while the velocity controller ensures that the end effector of the robot moves with a desired tangential velocity leading to task completion. This paper also provides a proof that the adaptive estimates of the radial direction converge to the actual radial vector. The performance of the control scheme is demonstrated in both simulation and on a real robot.

  • 142.
    Karayiannidis, Yiannis
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Smith, Christian
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Ögren, Petter
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Adaptive force/velocity control for opening unknown doors2012Inngår i: Robot Control, Volume 10, Part  1, 2012, s. 753-758Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The problem of door opening is fundamental for robots operating in domesticenvironments. Since these environments are generally unstructured, a robot must deal withseveral types of uncertainties associated with the dynamics and kinematics of a door to achievesuccessful opening. The present paper proposes a dynamic force/velocity controller which usesadaptive estimation of the radial direction based on adaptive estimates of the door hinge’sposition. The control action is decomposed into estimated radial and tangential directions,which are proved to converge to the corresponding actual values. The force controller usesreactive compensation of the tangential forces and regulates the radial force to a desired smallvalue, while the velocity controller ensures that the robot’s end-effector moves with a desiredtangential velocity. The performance of the control scheme is demonstrated in simulation witha 2 DoF planar manipulator opening a door.

  • 143.
    Kjellström, Hedvig
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Black, Michael J.
    Tracking People Interacting with Objects2010Inngår i: 2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2010, s. 747-754Konferansepaper (Fagfellevurdert)
    Abstract [en]

    While the problem of tracking 3D human motion has been widely studied, most approaches have assumed that the person is isolated and not interacting with the environment. Environmental constraints, however, can greatly constrain and simplify the tracking problem. The most studied constraints involve gravity and contact with the ground plane. We go further to consider interaction with objects in the environment. In many cases, tracking rigid environmental objects is simpler than tracking high-dimensional human motion. When a human is in contact with objects in the world, their poses constrain the pose of body, essentially removing degrees of freedom. Thus what would appear to be a harder problem, combining object and human tracking, is actually simpler. We use a standard formulation of the body tracking problem but add an explicit model of contact with objects. We find that constraints from the world make it possible to track complex articulated human motion in 3D from a monocular camera.

  • 144.
    Kjellström, Hedvig
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Romero, Javier
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Visual object-action recognition: Inferring object affordances from human demonstration2011Inngår i: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 115, nr 1, s. 81-90Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper investigates object categorization according to function, i.e., learning the affordances of objects from human demonstration. Object affordances (functionality) are inferred from observations of humans using the objects in different types of actions. The intended application is learning from demonstration, in which a robot learns to employ objects in household tasks, from observing a human performing the same tasks with the objects. We present a method for categorizing manipulated objects and human manipulation actions in context of each other. The method is able to simultaneously segment and classify human hand actions, and detect and classify the objects involved in the action. This can serve as an initial step in a learning from demonstration method. Experiments show that the contextual information improves the classification of both objects and actions.

  • 145.
    Kjellström, Hedvig
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Romero, Javier
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Visual Recognition of Grasps for Human-to-Robot Mapping2008Inngår i: 2008 IEEE/RSJ International Conference On Robots And Intelligent Systems, Vols 1-3, Conference Proceedings / [ed] Chatila, R; Kelly, A; Merlet, JP, 2008, s. 3192-3199Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a vision based method for grasp classification. It is developed as part of a Programming by Demonstration (PbD) system for which recognition of objects and pick-and-place actions represent basic building blocks for task learning. In contrary to earlier approaches, no articulated 3D reconstruction of the hand over time is taking place. The indata consists of a single image of the human hand. A 2D representation of the hand shape, based on gradient orientation histograms, is extracted from the image. The hand shape is then classified as one of six grasps by finding similar hand shapes in a large database of grasp images. The database search is performed using Locality Sensitive Hashing (LSH), an approximate k-nearest neighbor approach. The nearest neighbors also give an estimated hand orientation with respect to the camera. The six human grasps are mapped to three Barret hand grasps. Depending on the type of robot grasp, a precomputed grasp strategy is selected. The strategy is further parameterized by the orientation of the hand relative to the object. To evaluate the potential for the method to be part of a robust vision system, experiments were performed, comparing classification results to a baseline of human classification performance. The experiments showed the LSH recognition performance to be comparable to human performance.

  • 146.
    Kjellström, Hedvig
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Romero, Javier
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Martinez, David
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Simultaneous Visual Recognition of Manipulation Actions and Manipulated Objects2008Inngår i: Computer Vision - Eccv 2008, Pt Ii, Proceedings / [ed] Forsyth, D; Torr, P; Zisserman, A, 2008, Vol. 5303, s. 336-349Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The visual analysis of human manipulation actions is of interest for e.g. human-robot interaction applications where a robot learns how to perform a task by watching a human. In this paper, a method for classifying manipulation actions in the context of the objects manipulated, and classifying objects in the context of the actions used to manipulate them is presented. Hand and object features are extracted from the video sequence using a segmentation based approach. A shape based representation is used for both the hand and the object. Experiments show this representation suitable for representing generic shape classes. The action-object correlation over time is then modeled using conditional random fields. Experimental comparison show great improvement in classification rate when the action-object correlation is taken into account, compared to separate classification of manipulation actions and manipulated objects.

  • 147.
    Kokic, Mia
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.
    Stork, Johannes A.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.
    Haustein, Joshua A.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.
    Affordance Detection for Task-Specific Grasping Using Deep Learning2017Inngår i: 2017 IEEE-RAS 17TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTICS (HUMANOIDS), Institute of Electrical and Electronics Engineers (IEEE), 2017, s. 91-98Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper we utilize the notion of affordances to model relations between task, object and a grasp to address the problem of task-specific robotic grasping. We use convolutional neural networks for encoding and detecting object affordances, class and orientation, which we utilize to formulate grasp constraints. Our approach applies to previously unseen objects from a fixed set of classes and facilitates reasoning about which tasks an object affords and how to grasp it for that task. We evaluate affordance detection on full-view and partial-view synthetic data and compute task-specific grasps for objects that belong to ten different classes and afford five different tasks. We demonstrate the feasibility of our approach by employing an optimization-based grasp planner to compute task-specific grasps.

  • 148.
    Kootstra, Geert
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Wilming, N.
    Schmidt, N. M.
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    König, P.
    Learning and adaptation of sensorimotor contingencies: Prism-adaptation, a case study2012Inngår i: From Animals to Animats 12, Springer Berlin/Heidelberg, 2012, Vol. 7426 LNAI, s. 341-350Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper focuses on learning and adaptation of sensorimotor contingencies. As a specific case, we investigate the application of prism glasses, which change visual-motor contingencies. After an initial disruption of sensorimotor coordination, humans quickly adapt. However, scope and generalization of that adaptation is highly dependent on the type of feedback and exhibits markedly different degrees of generalization. We apply a model with a specific interaction of forward and inverse models to a robotic setup and subject it to the identical experiments that have been used on previous human psychophysical studies. Our model demonstrates both locally specific adaptation and global generalization in accordance with the psychophysical experiments. These results emphasize the role of the motor system for sensory processes and open an avenue to improve on sensorimotor processing.

  • 149.
    Kootstra, Gert
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Bergström, Niklas
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Fast and Automatic Detection and Segmentation of Unknown Objects2010Inngår i: Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids), IEEE , 2010, s. 442-447Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper focuses on the fast and automatic detection and segmentation of unknown objects in unknown environments. Many existing object detection and segmentation methods assume prior knowledge about the object or human interference. However, an autonomous system operating in the real world will often be confronted with previously unseen objects. To solve this problem, we propose a segmentation approach named Automatic Detection And Segmentation (ADAS). For the detection of objects, we use symmetry, one of the Gestalt principles for figure-ground segregation to detect salient objects in a scene. From the initial seed, the object is segmented by iteratively applying graph cuts. We base the segmentation on both 2D and 3D cues: color, depth, and plane information. Instead of using a standard grid-based representation of the image, we use super pixels. Besides being a more natural representation, the use of super pixels greatly improves the processing time of the graph cuts, and provides more noise-robust color and depth information. The results show that both the object-detection as well as the object-segmentation method are successful and outperform existing methods.

  • 150.
    Kootstra, Gert
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Bergström, Niklas
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Gestalt Principles for Attention and Segmentation in Natural and Artificial Vision Systems2011Inngår i: Semantic Perception, Mapping and Exploration (SPME), ICRA 2011 Workshop, eSMCs , 2011Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Gestalt psychology studies how the human visual system organizes the complex visual input into unitary elements. In this paper we show how the Gestalt principles for perceptual grouping and for figure-ground segregation can be used in computer vision. A number of studies will be shown that demonstrate the applicability of Gestalt principles for the prediction of human visual attention and for the automatic detection and segmentation of unknown objects by a robotic system.

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