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检索条件"机构=the Computer Vision and Active Perception Lab"

共 92 条记录，以下是31-40 订阅

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Integrating 3D features and virtual visual servoing for hand-eye and humanoid robot pose estimation

Integrating 3D features and virtual visual servoing for hand...

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Xavi Gratal Christian Smith Mårten Björkman Danica Kragic Computer Vision and Active Perception Lab. Royal Institute of Technology KTH Stockholm Sweden

In this paper, we propose an approach for vision-based pose estimation of a robot hand or full-body pose. The method is based on virtual visual servoing using a CAD model of the robot and it combines 2-D image features with depth features. The method can be applied to estimate either the pose of a robot hand or pose of the whole body given that its joint configuration is known. We present experimental results that show the performance of the approach as demonstrated on both a mobile humanoid robot and a stationary manipulator.

关键词： Robots Solid modeling Feature extraction Image edge detection Estimation Cameras Joints

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Performance analysis of stochastic behavior trees

Performance analysis of stochastic behavior trees

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Michele Colledanchise Alejandro Marzinotto Petter Ögren Computer Vision and Active Perception Lab. Royal Institute of Technology (KTH) Stockholm Sweden

This paper presents a mathematical framework for performance analysis of Behavior Trees (BTs). BTs are a recent alternative to Finite State Machines (FSMs), for doing modular task switching in robot control architectures. By encoding the switching logic in a tree structure, instead of distributing it in the states of a FSM, modularity and reusability are improved. In this paper, we compute performance measures, such as success/failure probabilities and execution times, for plans encoded and executed by BTs. To do this, we first introduce Stochastic Behavior Trees (SBT), where we assume that the probabilistic performance measures of the basic action controllers are given. We then show how Discrete Time Markov Chains (DTMC) can be used to aggregate these measures from one level of the tree to the next. The recursive structure of the tree then enables us to step by step propagate such estimates from the leaves (basic action controllers) to the root (complete task execution). Finally, we verify our analytical results using massive Monte Carlo simulations, and provide an illustrative example of the results for a complex robotic task.

关键词： Markov processes Robots Probabilistic logic Transient analysis Switches Vectors Reliability

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Mapping human intentions to robot motions via physical interaction through a jointly-held object

Mapping human intentions to robot motions via physical inter...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Yiannis Karayiannidis Christian Smith Danica Kragic Computer Vision and Active Perception Lab. Royal Institute of Technology (KTH) Stockholm Sweden

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.

关键词： Switches Force Robot sensing systems Admittance Rotation measurement Torque

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How Behavior Trees modularize robustness and safety in hybrid systems

How Behavior Trees modularize robustness and safety in hybri...

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2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Michele Colledanchise Petter Ögren Computer Vision and Active Perception Lab. KTH - Royal Institute of Technology Stockholm Sweden

Behavior Trees (BTs) have become a popular framework for designing controllers of in-game opponents in the computer gaming industry. In this paper, we formalize and analyze the reasons behind the success of the BTs using standard tools of robot control theory, focusing on how properties such as robustness and safety are addressed in a modular way. In particular, we show how these key properties can be traced back to the ideas of subsumption and sequential compositions of robot behaviors. Thus BTs can be seen as a recent addition to a long research effort towards increasing modularity, robustness and safety of robot control software. To illustrate the use of BTs, we provide a set of solutions to example problems.

关键词： Safety Robustness Robots Switches computers Games Vehicles

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Smartphone application for quantitative measurement of Parkinson tremors 6

Smartphone application for quantitative measurement of Parki...

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6th Latin American Congress on Biomedical Engineering, CLAIB 2014

作者： Benítez, George Enrique Figueras Parra, V. Huerta, M. Marzinotto, A. Clotet, R. González, R. Moreno, A. Pinto, K. Rivas, D. Alvizu, R. Sanchez, L.E. Network and Apply Telematic Group Simon Bolivar University Caracas Venezuela Universidad de las Fuerzas Armadas ESPE Sangolquí Ecuador Computer Vision and Active Perception Lab Royal Institute of Technology Stockholm Sweden Ecuador

ISBN: (纸本)9783319131160

One of the most common concerns in the atten-tion of patients with Parkinson's disease is an objectively eval-uation of the illness progress and the efficacy of treatments, in terms of the intensity and frequency of tremors. This symptom is produced by gradual degradation of the pigmented neurons located at the substantia nigra in the brain, in order to detect such movement levels. This paper proposes a Smartphone application for a quantitative detection, measurement and analysis of the Parkinson's tremor, due to the global use of Smartphones and the affordable cost of some Android plat-form devices. The subjects of this working project are those people who suffer from Parkinson's disease, who could down-load the application to their mobile phone in order to measure quantitatively the intensity and duration of their tremors, in any place, to send reports by email or to record them for a later use. The application enables the remote monitoring of the patients. © Springer International Publishing Switzerland 2015.

关键词： Smartphones

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Learning Predictive State Representations for planning

Learning Predictive State Representations for planning

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： J. A. Stork C. H. Ek D. Kragic Computer Vision and Active Perception Lab Centre for Autonomous Systems KTH Royal Institute of Technology Stockholm Sweden Computer Vision and Active Perception Lab Centre for Autonomous Systems School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

Predictive State Representations (PSRs) allow modeling of dynamical systems directly in observables and without relying on latent variable representations. A problem that arises from learning PSRs is that it is often hard to attribute semantic meaning to the learned representation. This makes generalization and planning in PSRs challenging. In this paper, we extend PSRs and introduce the notion of PSRs that include prior information (P-PSRs) to learn representations which are suitable for planning and interpretation. By learning a low-dimensional embedding of test features we map belief points of similar semantic to the same region of a subspace. This facilitates better generalization for planning and semantical interpretation of the learned representation. In specific, we show how to overcome the training sample bias and introduce feature selection such that the resulting representation emphasizes observables related to the planning task. We show that our P-PSRs result in qualitatively meaningful representations and present quantitative results that indicate improved suitability for planning.

关键词： Planning History Robots Semantics Predictive models Uncertainty

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A Transformation of the Position Based Visual Servoing Problem into a Convex Optimization Problem

A Transformation of the Position Based Visual Servoing Probl...

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IEEE Annual Conference on Decision and Control

作者： Yuquan Wang Johan Thunberg Xiaoming Hu Computer Vision and Active Perception Lab Royal Institute of Technology Department of Mathematics Royal Institute of Technology

ISBN: (纸本)9781467320658

Here we address the problem of moving a camera from an initial pose to a final pose. The trajectory between the two poses is subject to constraints on the camera motion and the visibility, where we have bounds on the allowed velocities and accelerations of the camera and require that a set of point features are visible for the camera. We assume that the pose is possible to retrieve from the observations of the point features, i.e., we have a Position Based Visual Servoing Problem with constraints. We introduce a two step method that transforms the problem into a convex optimization problem with linear constraints. In the first step the rotational motion is restricted to be of a certain type. This restriction allows us to retrieve an explicit solution of the rotational motion that is optimal in terms of minimizing geodesic distance. Furthermore, this restriction guarantees that the rotational motion satisfies the constraints. Using the explicit solution, we can formulate a convex optimization problem for the translational motion, where we include constraints on workspace and visibility.

关键词： Explicit Solution Cameras Visual servoing Constraint Convex function ROTATIONAL MOTION visibility translational motion Rotational Workplace acceleration

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A sensorimotor approach for self-learning of hand-eye coordination

A sensorimotor approach for self-learning of hand-eye coordi...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： A. Ghadirzadeh A. Maki M. Bjorkman Computer Vision and Active Perception Lab (CVAP) CSC KTH Royal Institute of Technology Stockholm Sweden

This paper presents a sensorimotor contingencies (SMC) based method to fully autonomously learn to perform hand-eye coordination. We divide the task into two visuomotor subtasks, visual fixation and reaching, and implement these on a PR2 robot assuming no prior information on its kinematic model. Our contributions are three-fold: i) grounding a robot in the environment by exploiting SMCs in the action planning system, which eliminates the need for prior knowledge of the kinematic or dynamic models of the robot;ii) using a forward model to search for proper actions to solve the task by minimizing a cost function, instead of training a separate inverse model, to speed up training;iii) encoding 3D spatial positions of a target object based on the robot's joint positions, thus avoiding calibration with respect to an external coordinate system. The method is capable of learning the task of hand-eye coordination from scratch by less than 20 sensory-motor pairs that are iteratively generated at real-time speed. In order to examine the robustness of the method while dealing with nonlinear image distortions, we apply a so-called retinal mapping image deformation to the input images. Experimental results show the successfulness of the method even under considerable image deformations.

关键词： Robot sensing systems Predictive models Robot kinematics Training Mathematical model Cost function

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From object categories to grasp transfer using probabilistic reasoning

From object categories to grasp transfer using probabilistic...

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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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A sensorimotor reinforcement learning framework for physical Human-Robot Interaction

A sensorimotor reinforcement learning framework for physical...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Ali Ghadirzadeh Judith Butepage Atsuto Maki Danica Kragic Marten Bjorkman Computer Vision and Active Perception Lab (CVAP) CSC KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781509037636

Modeling of physical human-robot collaborations is generally a challenging problem due to the unpredictive nature of human behavior. To address this issue, we present a data-efficient reinforcement learning framework which enables a robot to learn how to collaborate with a human partner. The robot learns the task from its own sensorimotor experiences in an unsupervised manner. The uncertainty in the interaction is modeled using Gaussian processes (GP) to implement a forward model and an action-value function. Optimal action selection given the uncertain GP model is ensured by Bayesian optimization. We apply the framework to a scenario in which a human and a PR2 robot jointly control the ball position on a plank based on vision and force/torque data. Our experimental results show the suitability of the proposed method in terms of fast and data-efficient model learning, optimal action selection under uncertainty and equal role sharing between the partners.

关键词： Robot sensing systems Predictive models Trajectory Impedance Learning (artificial intelligence) Uncertainty

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