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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Kaiyu Hang Florian T. Pokorny Danica Kragic Computer Vision and Active Perception Lab Centre for Autonomous Systems CSC KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467363563

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.

关键词： Friction Coefficients Grasp Synthesis underlying friction coefficients Grasp coefficient of friction friction synthesis

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Interactive object classification using sensorimotor contingencies

Interactive object classification using sensorimotor conting...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

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

ISBN: (纸本)9781467363563

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.

关键词： Robot sensing systems Shape Data models Predictive models Gaussian processes Uncertainty

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Interaction aware trajectory planning for merge scenarios in congested traffic situations

Interaction aware trajectory planning for merge scenarios in...

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International Conference on Intelligent Transportation

作者： Niclas Evestedt Erik Ward John Folkesson Daniel Axehill Division of Automatic Control Linköping University Sweden Computer Vision and Active Perception Lab KTH Stockholm Sweden

ISBN: (纸本)9781509018901

In many traffic situations there are times where interaction with other drivers is necessary and unavoidable in order to safely progress towards an intended destination. This is especially true for merge manoeuvres into dense traffic, where drivers sometimes must be somewhat aggressive and show the intention of merging in order to interact with the other driver and make the driver open the gap needed to execute the manoeuvre safely. Many motion planning frameworks for autonomous vehicles adopt a reactive approach where simple models of other traffic participants are used and therefore need to adhere to large margins in order to behave safely. However, the large margins needed can sometimes get the system stuck in congested traffic where time gaps between vehicles are too small. In other situations, such as a highway merge, it can be significantly more dangerous to stop on the entrance ramp if the gaps are found to be too small than to make a slightly more aggressive manoeuvre and let the driver behind open the gap needed. To remedy this problem, this work uses the Intelligent Driver Model (IDM) to explicitly model the interaction of other drivers and evaluates the risk by their required deceleration in a similar manner as the Minimum Overall Breaking Induced by Lane change (MOBIL) model that has been used in large scale traffic simulations before. This allows the algorithm to evaluate the effect on other drivers depending on our own trajectory plans by simulating the nearby traffic situation. Finding a globally optimal solution is often intractable in these situations so instead a large set of candidate trajectories are generated that are evaluated against the traffic scene by forward simulations of other traffic participants. By discretization and using an efficient trajectory generator together with efficient modelling of the traffic scene real-time demands can be met.

关键词： Trajectory Vehicles Planning Acceleration Predictive models Roads Merging

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Integrated Motion and Clasp Planning with Virtual Linking

Integrated Motion and Clasp Planning with Virtual Linking

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Johannes A. Stork Florian T. Pokorny Danica Kragic Computer Vision and Active Perception Lab Centre for Autonomous Systems CSC KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467363563

In this work, we address the problem of simultaneous clasp and motion planning on unknown objects with holes. Clasping an object enables a rich set of activities such as dragging, toting, pulling and hauling which can be applied to both soft and rigid objects. To this end, we define a virtual linking measure which characterizes the spacial relation between the robot hand and object. The measure utilizes a set of closed curves arising from an approximately shortest basis of the object's first homology group. We define task spaces to perform collision-free motion planing with respect to multiple prioritized objectives using a sampling-based planing method. The approach is tested in simulation using different robot hands and various real-world objects.

关键词： Integrated Motion Clasp Planning Virtual Linking robotic hand Hooks Yard motion planning Planing Virtual Holes Closed curve

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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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Cooperative Object Path Following Control by means of Mobile Manipulators: a Switched Systems Approach

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IFAC Proceedings Volumes 2012年第22期45卷 773-778页

作者： Johan Markdahl Yiannis Karayiannidis Xiaoming Hu Division of Optimization and Systems Theory KTH Royal Institute of Technology Stockholm Sweden Computer Vision and Active Perception Lab KTH Royal Institute of Technology Stockholm Sweden

This paper proposes a switched control algorithm for distributed cooperative manipulation of rigid bodies in a planar setting. More specifically, we consider the problem of making a grasped object follow a desired position and orientation path. The system contains N robots, possibly of heterogeneous designs, each of which consists of a manipulator arm and a nonholonomic mobile platform. Control is based on local information, is carried out on a kinematic level and partly utilizes inverse kinematics. We use Lyapunov-like arguments to prove that the algorithm is almost globally stable and also show that its parameters can be chosen so that any input saturation level is met. The time between certain switches is proved to be bounded below, and the system is shown to be free of any Zeno behavior.

关键词： Cooperative Manipulation Mobile Manipulation Switched Systems

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Integrated On-line Robot-camera Calibration and Object Pose Estimation

Integrated On-line Robot-camera Calibration and Object Pose ...

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IEEE International Conference on Robotics and Automation

作者： Karl Pauwels Danica Kragic Computer Vision and Active Perception Lab Center for Autonomous Systems School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467380270

We present a novel on-line approach for extrinsic robot-camera calibration, a process often referred to as hand-eye calibration, that uses object pose estimates from a real-time model-based tracking approach. While off-line calibration has seen much progress recently due to the incorporation of bundle adjustment techniques, on-line calibration still remains a largely open problem. Since we update the calibration in each frame, the improvements can be incorporated immediately in the pose estimation itself to facilitate object tracking. Our method does not require the camera to observe the robot or to have markers at certain fixed locations on the robot. To comply with a limited computational budget, it maintains a fixed size configuration set of samples. This set is updated each frame in order to maximize an observability criterion. We show that a set of size 20 is sufficient in real-world scenarios with static and actuated cameras. With this set size, only 100 microseconds are required to update the calibration in each frame, and we typically achieve accurate robot-camera calibration in 10 to 20 seconds. Together, these characteristics enable the incorporation of calibration in normal task execution.

关键词： Calibration Object Pose

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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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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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Multivariate Discretization for Bayesian Network Structure Learning in Robot Grasping

Multivariate Discretization for Bayesian Network Structure L...

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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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