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Enhancing Visual perception of Shape through Tactile Glances

Enhancing Visual Perception of Shape through Tactile Glances

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Marten Bjorkman Yasemin Bekiroglu Virgile Hogman Danica Kragic Centre for Autonomous Systems and the Computer Vision and Active Perception Lab CSC KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467363563

Object shape information is an important parameter in robot grasping tasks. However, it may be difficult to obtain accurate models of novel objects due to incomplete and noisy sensory measurements. In addition, object shape may change due to frequent interaction with the object (cereal boxes, etc). In this paper, we present a probabilistic approach for learning object models based on visual and tactile perception through physical interaction with an object. Our robot explores unknown objects by touching them strategically at parts that are uncertain in terms of shape. The robot starts by using only visual features to form an initial hypothesis about the object shape, then gradually adds tactile measurements to refine the object model. Our experiments involve ten objects of varying shapes and sizes in a real setup. The results show that our method is capable of choosing a small number of touches to construct object models similar to real object shapes and to determine similarities among acquired models.

关键词： Shape Robot sensing systems Visualization Cameras Three-dimensional displays Stereo vision

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Task-based grasp adaptation on a humanoid robot

Task-based grasp adaptation on a humanoid robot

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10th IFAC Symposium on Robot Control, SYROCO 2012

作者： Bohg, Jeannette Welke, Kai León, Beatriz Do, Martin Song, Dan Wohlkinger, Walter Madry, Marianna Aldóma, Aitor Przybylski, Markus Asfour, Tamim Martí, Higinio Kragic, Danica Morales, Antonio Vincze, Markus Humanoids and Intelligence Systems Lab. Institute for Anthropomatics KIT Germany Autonomous Motion Lab. MPI for Intelligent Systems Germany Robotic Intelligence Laboratory Department of Computer Science and Engineering UJI Spain Computer Vision and Active Perception Lab. KTH Sweden Automation and Control Institute TUW Austria

ISBN: (纸本)9783902823113

In this paper, we present an approach towards autonomous grasping of objects according to their category and a given task. Recent advances in the field of object segmentation and categorization as well as task-based grasp inference have been leveraged by integrating them into one pipeline. This allows us to transfer task-specific grasp experience between objects of the same category. The effectiveness of the approach is demonstrated on the humanoid robot ARMAR-IIIa. © 2012 IFAC.

关键词： Anthropomorphic robots

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Extracting Essential Local Object Characteristics for 3D Object Categorization

Extracting Essential Local Object Characteristics for 3D Obj...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Marianna Madry Heydar Maboudi Afkham Carl Henrik Ek Stefan Carlsson Danica Kragic Centre for Autonomous Systems and the Computer Vision and Active Perception Lab CSC KTH Royal Institute of Technology Stockholm Sweden.

ISBN: (纸本)9781467363563

Most object classes share a considerable amount of local appearance and often only a small number of features are discriminative. The traditional approach to represent an object is based on a summarization of the local characteristics by counting the number of feature occurrences. In this paper we propose the use of a recently developed technique for summarizations that, rather than looking into the quantity of features, encodes their quality to learn a description of an object. Our approach is based on extracting and aggregating only the essential characteristics of an object class for a task. We show how the proposed method significantly improves on previous work in 3D object categorization. We discuss the benefits of the method in other scenarios such as robot grasping. We provide extensive quantitative and qualitative experiments comparing our approach to the state of the art to justify the described approach.

关键词： 3D Object Categorization traditional approach represent

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Multivariate Discretization for Bayesian Network Structure Learning in Robot Grasping

Multivariate Discretization for Bayesian Network Structure L...

引用

2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

作者： Dan Song Carl Henrik Ek Kai Huebner Danica Kragic KTH - Royal Institute of Technology StockholmSwedenas members of the Computer Vision & Active Perception Lab.Centre for Autonomous Systems

A major challenge in modeling with BNs is learning the structure from both discrete and multivariate continuous data. A common approach in such situations is to discretize continuous data before structure learning. However efficient methods to discretize high-dimensional variables are largely lacking. This paper presents a novel method specifically aiming at discretization of high-dimensional, high-correlated data. The method consists of two integrated steps: non-linear dimensionality reduction using sparse Gaussian process latent variable models, and discretization by application of a mixture model. The model is fully probabilistic and capable to facilitate structure learning from discretized data, while at the same time retain the continuous representation. We evaluate the effectiveness of the method in the domain of robot grasping. Compared with traditional discretization schemes, our model excels both in task classification and prediction of hand grasp configurations. Further, being a fully probabilistic model it handles uncertainty in the data and can easily be integrated into other frameworks in a principled manner.

关键词： Data models Feature extraction Robot sensing systems Principal component analysis Grasping Humans

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Persistent Homology for Learning Densities with Bounded Support 12

Persistent Homology for Learning Densities with Bounded Supp...

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Annual Conference on Neural Information Processing systems

作者： Florian T. Pokorny Carl Henrik Ek Hedvig Kjellstroem Danica Kragic Computer Vision and Active Perception Lab Centre for Autonomous Systems School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781627480031

We present a novel method for learning densities with bounded support which enables us to incorporate 'hard' topological constraints. In particular, we show how emerging techniques from computational algebraic topology and the notion of persistent homology can be combined with kemel-based methods from machine learning for the purpose of density estimation. The proposed formalism facilitates learning of models with bounded support in a principled way, and - by incorporating persistent homology techniques in our approach - we are able to encode algebraic-topological constraints which are not addressed in current state of the art probabilistic models. We study the behaviour of our method on two synthetic examples for various sample sizes and exemplify the benefits of the proposed approach on a real-world dataset by learning a motion model for a race car. We show how to leam a model which respects the underlying topological structure of the racetrack, constraining the trajectories of the car.

关键词： racing cars Learning Bounded topological structure Sample Size Cars current status Algebraic Topology

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Initialization and System Modeling in 3-D Pose Tracking

Initialization and System Modeling in 3-D Pose Tracking

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International Conference on Pattern Recognition

作者： D. Kragic V. Kyrki Computational Vision and Active Perception KTH Centre for Autonomous Systems Sweden Laboratory of Information Processing Lappeenranta University of Technology Finland

Initialization and choice of adequate motion models are two important but seldom discussed problems in 3D model-based pose (position and orientation) tracking. In this paper, we propose an automatic initialization approach suitable for textured objects. In addition, we define, study and experimentally evaluate three motion models commonly used in visual servoing and augmented reality

关键词： Modeling Cameras Histograms Visual servoing Predictive models Tracking Augmented reality computer vision Laboratories Information processing

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Hierarchical Fingertip Space for Multi-fingered Precision Grasping

Hierarchical Fingertip Space for Multi-fingered Precision Gr...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Kaiyu Hang Johannes A. Stork Danica Kragic Computer Vision and Active Perception Lab Centre for Autonomous Systems School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781479969357

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.

关键词： Optimization Three-dimensional displays Joints Robots Grasping Encoding Extraterrestrial measurements

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Modeling motion patterns of dynamic objects by IOHMM

Modeling motion patterns of dynamic objects by IOHMM

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Zhan Wang Rares Ambrus Patric Jensfelt John Folkesson Computer Vision and Active Perception Lab Centre for Autonomous Systems CSC KTH Royal Institute of Technology Stockholm 100 44 Sweden

ISBN: (纸本)9781479969357

This paper presents a novel approach to model motion patterns of dynamic objects, such as people and vehicles, in the environment with the occupancy grid map representation. Corresponding to the ever-changing nature of the motion pattern of dynamic objects, we model each occupancy grid cell by an IOHMM, which is an inhomogeneous variant of the HMM. This distinguishes our work from existing methods which use the conventional HMM, assuming motion evolving according to a stationary process. By introducing observations of neighbor cells in the previous time step as input of IOHMM, the transition probabilities in our model are dependent on the occurrence of events in the cell's neighborhood. This enables our method to model the spatial correlation of dynamics across cells. A sequence processing example is used to illustrate the advantage of our model over conventional HMM based methods. Results from the experiments in an office corridor environment demonstrate that our method is capable of capturing dynamics of such human living environments.

关键词： patterning of movement Stationary Process Environments TRANSITION PROBABILITIES Vehicle Occupancy

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Learning the tactile signatures of prototypical object parts for robust part-based grasping of novel objects

Learning the tactile signatures of prototypical object parts...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Emil Hyttinen Danica Kragic Renaud Detry the Centre for Autonomous Systems and the Computer Vision and Active Perception Lab KTH Royal Institute of Technology Stockholm Sweden the University of Liège Belgium

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.

关键词： Shape Prototypes Stability analysis Joints Grasping Robot sensing systems

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Enhanced visual scene understanding through human-robot dialog

Enhanced visual scene understanding through human-robot dial...

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2011 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Matthew Johnson-Roberson Jeannette Bohg Gabriel Skantze Joakim Gustafson Rolf Carlson Babak Rasolzadeh Danica Kragic Centre of Autonomous Systems Computational Vision and Active Perception Laboratory Sweden Department for Speech Music and Hearing Institutes part of School of Computer Science KTH in Stockholm Sweden

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.

关键词： Robots Entropy Humans Three dimensional displays Histograms Machine vision Image analysis

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