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From object categories to grasp transfer using probabilistic reasoning

From object categories to grasp transfer using probabilistic...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Marianna Madry Dan Song Danica Kragic Computer Vision and Active Perception Lab Center for Autonomous Systems KTH Royal Institute of Technology Sweden

In this paper we address the problem of grasp generation and grasp transfer between objects using categorical knowledge. The system is built upon an i) active scene segmentation module, able of generating object hypotheses and segmenting them from the background in real time, ii) object categorization system using integration of 2D and 3D cues, and iii) probabilistic grasp reasoning system. Individual object hypotheses are first generated, categorized and then used as the input to a grasp generation and transfer system that encodes task, object and action properties. The experimental evaluation compares individual 2D and 3D categorization approaches with the integrated system, and it demonstrates the usefulness of the categorization in task-based grasping and grasp transfer.

关键词： Cognition Robots Kernel

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Learning a dictionary of prototypical grasp-predicting parts from grasping experience

Learning a dictionary of prototypical grasp-predicting parts...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Renaud Detry Carl Henrik Ek Marianna Madry Danica Kragic Centre for Autonomous Systems and the Computer Vision and Active Perception Laboratory CSC Royal Institute of Technology Stockholm Sweden

We present a real-world robotic agent that is capable of transferring grasping strategies across objects that share similar parts. The agent transfers grasps across objects by identifying, from examples provided by a teacher, parts by which objects are often grasped in a similar fashion. It then uses these parts to identify grasping points onto novel objects. We focus our report on the definition of a similarity measure that reflects whether the shapes of two parts resemble each other, and whether their associated grasps are applied near one another. We present an experiment in which our agent extracts five prototypical parts from thirty-two real-world grasp examples, and we demonstrate the applicability of the prototypical parts for grasping novel objects.

关键词： Robots Shape Prototypes Three-dimensional displays Grasping Dictionaries Training

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Sparse summarization of robotic grasping data

Sparse summarization of robotic grasping data

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Martin Hjelm Carl Henrik Ek Renaud Detry Hedvig Kjellström Danica Kragic Centre for Autonomous Systems Computer Vision and Active Perception Laboratory CSC Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467356404

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.

关键词： Robots Data models Grasping Shape Kernel Optimization Encoding

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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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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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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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Visual recognition of grasps for human-to-robot mapping

Visual recognition of grasps for human-to-robot mapping

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2008 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Hedvig Kjellstrom Javier Romero Danica Kragic Computational Vision and Active Perception Laboratory Centre for Autonomous Systems School of Computer Science and Communication KTH Stockholm Sweden

ISBN: (纸本)9781424420575

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.

关键词： Robots Three dimensional displays Image segmentation Image reconstruction Humans Robot sensing systems Distance measurement

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Improving generalization for 3D object categorization with Global Structure Histograms

Improving generalization for 3D object categorization with G...

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2012 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Marianna Madry Carl Henrik Ek Renaud Detry Kaiyu Hang Danica Kragic Centre for Autonomous Systems and the Computer Vision and Active Perception Laboratory CSC KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467317375

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

关键词： Histograms Robustness Encoding Databases Image color analysis Shape Data models

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Modeling and recognition of actions through motor primitives

Modeling and recognition of actions through motor primitives

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IEEE International Conference on Robotics and Automation (ICRA)

作者： David Martinez Danica Kragic Computational Vision and Active Perception Lab Centre for Autonomous Systems KTH Royal Institute of Technology Stockholm Sweden

We investigate modeling and recognition of object manipulation actions for the purpose of imitation based learning in robotics. To model the process, we are using a combination of discriminative (support vector machines, conditional random fields) and generative approaches (hidden Markov models). We examine the hypothesis that complex actions can be represented as a sequence of motion or action primitives. The experimental evaluation, performed with five object manipulation actions and 10 people, investigates the modeling approach of the primitive action structure and compares the performance of the considered generative and discriminative models.

关键词： Hidden Markov models Support vector machines Humans Support vector machine classification Robotics and automation Magnetic sensors Biological system modeling computer vision Robot sensing systems USA Councils

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