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Task-based Grasp Adaptation on a Humanoid Robot

IFAC Proceedings Volumes 2012年第22期45卷 779-786页

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

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.

关键词： Robotic Grasping and Manipulation Task-based Grasp Synthesis Visual Servoing Attention Segmentation Object categorization System Integration

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Grasp recognition and mapping on humanoid robots

Grasp recognition and mapping on humanoid robots

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IEEE-RAS International Conference on Humanoid Robots

作者： Martin Do Javier Romero Hedvig Kjellström Pedram Azad Tamim Asfour Danica Kragic Rüdiger Dillmann Institute for Anthropomatics University of Karlsruhe Karlsruhe Germany Royal Institute of Technology Computer Vision & Active Perception Laboratory Centre for Autonomous Systems KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781424445875

In this paper, we present a system for vision-based grasp recognition, mapping and execution on a humanoid robot to provide an intuitive and natural communication channel between humans and humanoids. This channel enables a human user to teach a robot how to grasp an object. The system comprises three components: human upper body motion capture system which provides the approaching direction towards an object, hand pose estimation and grasp recognition system, which provides the grasp type performed by the human as well as a grasp mapping and execution system for grasp reproduction on a humanoid robot with five-fingered hands. All three components are real-time and markerless. Once an object is reached, the hand posture is estimated, including hand orientation and grasp type. For the execution on a robot, hand posture and approach movement are mapped and optimized according to the kinematic limitations of the robot. Experimental results are performed on the humanoid robot ARMAR-IIIb.

关键词： Humanoid robots Humans Educational robots Robot vision systems Communication channels Motion estimation Kinematics Robot programming Education Anthropomorphism

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What's in the Container? Classifying Object Contents from vision and Touch

What's in the Container? Classifying Object Contents from Vi...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Puren Guler Yasemin Bekiroglu Xavi Gratal 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 Computer Architecture and Technology Department University of Granada Spain

ISBN: (纸本)9781479969357

Robots operating in household environments need to interact with food containers of different types. Whether a container is filled with milk, juice, yogurt or coffee may affect the way robots grasp and manipulate the container. In this paper, we concentrate on the problem of identifying what kind of content is in a container based on tactile and/or visual feedback in combination with grasping. In particular, we investigate the benefits of using unimodal (visual or tactile) or bimodal (visual-tactile) sensory data for this purpose. We direct our study toward cardboard containers with liquid or solid content or being empty. The motivation for using grasping rather than shaking is that we want to investigate the content prior to applying manipulation actions to a container. Our results show that we achieve comparable classification rates with unimodal data and that the visual and tactile data are complimentary.

关键词： CONTAINERS Grasp Robot Food Containers dry matter content NONVOLATILE CONTENT Visualization Unimodal

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Saliency ranking for benthic survey using underwater images

Saliency ranking for benthic survey using underwater images

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International Conference on Control, Automation, Robotics and vision (ICARCV)

作者： Matthew Johnson-Roberson Oscar Pizarro Stefan Williams Computational Vision and Active Perception Laboratory School of Computer Science and Communication Royal Institute of Technology Sweden ARC Centre of Excellence of Autonomous Systems Australian Centre of Field Robotics University of Sydney Australia

This paper presents a novel architecture for a classification system based on the visual saliency of images. The work is motivated by the difficulty of reviewing large numbers of images as a human operator in the context of autonomous Underwater Vehicle (AUV) surveys. We formulate a feature space in which an algorithm operates over color and texture to determine saliency and illustrate how this can be used to find interesting or unusual images within a large data set. The saliency classification based on these general image features allows for overlays highlighting interesting benthos or geologic structures on large scale 3D seafloor reconstructions, quickly providing spatial context to human observers. These results are validated using a set of human trials in which images are classified into salient and non-salient categories by a number of test subjects. The trials show good agreement both between subjects and between the human labels and the automated classification system. The results of the automated technique are also compared directly to a more traditional SVM classification system showing favorable results for our system for generalizing to new environments.

关键词： Pixel Humans Visualization Entropy Image color analysis Indexes Context

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Model-aided State Estimation for Quadrotor Micro Air Vehicles amidst Wind Disturbances

Model-aided State Estimation for Quadrotor Micro Air Vehicle...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Dinuka Abeywardena Zhan Wang Gamini Dissanayake Steven L. Waslander Sarath Kodagoda Centre for Autonomous Systems University of Technology Sydney Computer Vision and Active Perception Lab Centre for Autonomous Systems CSC The Royal Institute of Technology KTH Stockholm 100 44 Sweden Waterloo Autonomous Vehicles Laboratory University of Waterloo Waterloo ON Canada N2L 3G1

ISBN: (纸本)9781479969357

This paper extends the recently developed Model-Aided Visual-Inertial Fusion (MA-VIF) technique for quadrotor Micro Air Vehicles (MAV) to deal with wind disturbances. The wind effects are explicitly modelled in the quadrotor dynamic equations excluding the unobservable wind velocity component. This is achieved by a nonlinear observability of the dynamic system with wind effects. We show that using the developed model, the vehicle pose and two components of the wind velocity vector can be simultaneously estimated with a monocular camera and an inertial measurement unit. We also show that the MA-VIF is reasonably tolerant to wind disturbances, even without explicit modelling of wind effects and explain the reasons for this behaviour. Experimental results using a Vicon motion capture system are presented to demonstrate the effectiveness of the proposed method and validate our claims.

关键词： Mathematical model Wind speed Equations Observability Accelerometers Vectors Vehicle dynamics

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Object recognition using saliency maps and HTM learning

Object recognition using saliency maps and HTM learning

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IEEE International Workshop on Imaging systems and Techniques (IST)

作者： Ioannis Kostavelis Lazaros Nalpantidis Antonios Gasteratos Laboratory of Robotics and Automation Department of Production and Management Engineering Democritus University of Thrace Xanthi Greece Computer Vision and Active Perception Laboratory Centre for Autonomous Systems Royal Institute of Technology Stockholm Sweden

In this paper a pattern classification and object recognition approach based on bio-inspired techniques is presented. It exploits the Hierarchical Temporal Memory (HTM) topology, which imitates human neocortex for recognition and categorization tasks. The HTM comprises a hierarchical tree structure that exploits enhanced spatiotemporal modules to memorize objects appearing in various orientations. In accordance with HTM's biological inspiration, human vision mechanisms can be used to preprocess the input images. Therefore, the input images undergo a saliency computation step, revealing the plausible information of the scene, where a human might fixate. The adoption of the saliency detection module releases the HTM network from memorizing redundant information and augments the classification accuracy. The efficiency of the proposed framework has been experimentally evaluated in the ETH-80 dataset, and the classification accuracy has been found to be greater than other HTM systems.

关键词： Correlation Quantization Vectors Accuracy Support vector machines Object recognition Humans

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A person following behaviour for a mobile robot

A person following behaviour for a mobile robot

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IEEE International Conference on Robotics and Automation (ICRA)

作者： H. Sidenbladh D. Kragic H.I. Christensen Computer Vision and Active Perception Lab Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden Centre for Autonomous Systems Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden

In this paper, a person following behaviour for a mobile robot is presented. The head of the person is located using skin colour detection. Then, a control loop is fed with the camera movements required to put the upper part of the person in the center of the image. The algorithm was tested in different rooms of a research lab. It performed well in all lighting conditions except in direct sunlight. Since the background and lighting cannot be controlled, the vision algorithm must be robust to such changes. However, since the computing power is quite limited, the algorithm must have as low complexity as possible.

关键词： Mobile robots Robot vision systems Cameras computer vision Skin Image motion analysis Numerical analysis Lighting control Humans Machine vision

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RCAMP: A resilient communication-aware motion planner for mobile robots with autonomous repair of wireless connectivity

arXiv

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arXiv 2017年

作者： Caccamo, Sergio Parasuraman, Ramviyas Freda, Luigi Gianni, Mario Ögren, Petter Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication KTH Royal Institute of Technology Sweden Purdue University West Lafayette United States ALCOR Laboratory DIAG Sapienza University of Rome Italy

Mobile robots, be it autonomous or teleoperated, require stable communication with the base station to exchange valuable information. Given the stochastic elements in radio signal propagation, such as shadowing and fading, and the possibilities of unpredictable events or hardware failures, communication loss often presents a significant mission risk, both in terms of probability and impact, especially in Urban Search and Rescue (USAR) operations. Depending on the circumstances, disconnected robots are either abandoned, or attempt to autonomously back-trace their way to the base station. Although recent results in Communication-Aware Motion Planning can be used to effectively manage connectivity with robots, there are no results focusing on autonomously re-establishing the wireless connectivity of a mobile robot without back-tracing or using detailed a priori information of the network. In this paper, we present a robust and online radio signal mapping method using Gaussian Random Fields, and propose a Resilient Communication-Aware Motion Planner (RCAMP) that integrates the above signal mapping framework with a motion planner. RCAMP considers both the environment and the physical constraints of the robot, based on the available sensory information. We also propose a self-repair strategy using RCMAP, that takes both connectivity and the goal position into account when driving to a connection-safe position in the event of a communication loss. We demonstrate the proposed planner in a set of realistic simulations of an exploration task in single or multi-channel communication scenarios. Copyright © 2017, The Authors. All rights reserved.

关键词： Mobile robots

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Predicting human intention in visual observations of hand/object interactions

Predicting human intention in visual observations of hand/ob...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Dan Song Nikolaos Kyriazis Iason Oikonomidis Chavdar Papazov Antonis Argyros Darius Burschka Danica Kragic Computer Vision & Active Perception Laboratory Centre for Autonomous Systems Royal Institute of Technology Stockholm Sweden Institute of Computer Science Foundation for Research and Technology-Hellas Crete Greece Technische Universität München Munich Germany

ISBN: (纸本)9781467356404

The main contribution of this paper is a probabilistic method for predicting human manipulation intention from image sequences of human-object interaction. Predicting intention amounts to inferring the imminent manipulation task when human hand is observed to have stably grasped the object. Inference is performed by means of a probabilistic graphical model that encodes object grasping tasks over the 3D state of the observed scene. The 3D state is extracted from RGB-D image sequences by a novel vision-based, markerless hand-object 3D tracking framework. To deal with the high-dimensional state-space and mixed data types (discrete and continuous) involved in grasping tasks, we introduce a generative vector quantization method using mixture models and self-organizing maps. This yields a compact model for encoding of grasping actions, able of handling uncertain and partial sensory data. Experimentation showed that the model trained on simulated data can provide a potent basis for accurate goal-inference with partial and noisy observations of actual real-world demonstrations. We also show a grasp selection process, guided by the inferred human intention, to illustrate the use of the system for goal-directed grasp imitation.

关键词： Grasping Three-dimensional displays Data models Robot sensing systems Bayes methods Computational modeling

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Tracking techniques for visual servoing tasks

Tracking techniques for visual servoing tasks

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： D. Kragic H.I. Christensen Computer Vision and Active Perception Lab Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden Centre for Autonomous Sys Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden

Many of today's visual servoing systems rely on the use of markers on the object to provide features for control. There is thus a need for a visual system that provides control features regardless of the appearance of the object. Region based tracking is a natural approach since it does not require any special type of features. In this paper we present two different approaches to region based tracking: 1) a multi-resolution gradient based approach (using optical flow); and 2) a discrete feature based search approach. We present experiments conducted with both techniques for different types of image motions. Finally, the performance, drawbacks and limitations of used techniques are discussed.

关键词： Visual servoing Robots Tracking computer vision Numerical analysis Control systems Grippers Image motion analysis Motion estimation Visual system

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