Püren Güler | Örebro University (original) (raw)

Papers by Püren Güler

Research paper thumbnail of Active Perception and Modeling of Deformable Surfaces using Gaussian Processes and Position-based Dynamics

Exploring and modeling heterogeneous elastic surfaces requires multiple interactions with the env... more Exploring and modeling heterogeneous elastic surfaces requires multiple interactions with the environment and a complex selection of physical material parameters. The most common approaches model deformable properties from sets of offline observations using computationally expensive force-based simulators. In this work we present an online probabilistic framework for autonomous estimation of a deformability distribution map of heterogeneous elastic surfaces from few physical interactions. The method takes advantage of Gaussian Processes for constructing a model of the environment geometry surrounding a robot. A fast Position-based Dynamics simulator uses focused environmental observations in order to model the elastic behavior of portions of the environment. Gaussian Process Regression maps the local deformability on the whole environment in order to generate a deformability distribution map. We show experimental results using a PrimeSense camera, a Kinova Jaco2 robotic arm and an O...

Research paper thumbnail of Modeling of Deformable Objects for Robotic Manipulation: A Tutorial and Review

Frontiers in Robotics and AI

Manipulation of deformable objects has given rise to an important set of open problems in the fie... more Manipulation of deformable objects has given rise to an important set of open problems in the field of robotics. Application areas include robotic surgery, household robotics, manufacturing, logistics, and agriculture, to name a few. Related research problems span modeling and estimation of an object's shape, estimation of an object's material properties, such as elasticity and plasticity, object tracking and state estimation during manipulation, and manipulation planning and control. In this survey article, we start by providing a tutorial on foundational aspects of models of shape and shape dynamics. We then use this as the basis for a review of existing work on learning and estimation of these models and on motion planning and control to achieve desired deformations. We also discuss potential future lines of work.

Research paper thumbnail of Active perception and modeling of deformable surfaces using Gaussian processes and position-based dynamics

2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), Nov 1, 2016

Exploring and modeling heterogeneous elastic surfaces requires multiple interactions with the env... more Exploring and modeling heterogeneous elastic surfaces requires multiple interactions with the environment and a complex selection of physical material parameters. The most common approaches model deformable properties from sets of offline observations using computationally expensive forcebased simulators. In this work we present an online probabilistic framework for autonomous estimation of a deformability distribution map of heterogeneous elastic surfaces from few physical interactions. The method takes advantage of Gaussian Processes for constructing a model of the environment geometry surrounding a robot. A fast Position-based Dynamics simulator uses focused environmental observations in order to model the elastic behavior of portions of the environment. Gaussian Process Regression maps the local deformability on the whole environment in order to generate a deformability distribution map. We show experimental results using a PrimeSense camera, a Kinova Jaco2 robotic arm and an Optoforce sensor on different deformable surfaces.

Research paper thumbnail of Real-Time Global Anomaly Detection for Crowd Video Surveillance Using SIFT

5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013), 2013

Automated analysis of crowd behaviour using surveillance videos is an important issue for public ... more Automated analysis of crowd behaviour using surveillance videos is an important issue for public security as it allows detection of potentially dangerous situations in crowds. Although there is a considerable amount of study in crowd behaviour analysis, the majority are limited in several ways. A few problems to mention are: limited real-time considerations, requirement of pre-set rigid anomaly rules, and high algorithm complexity. In this paper, we propose a Scale Invariant Feature Transform (SIFT) based holistic approach which is able to run in real time to detect global anomalies. Events which deviate significantly from the normal behaviour in the data set (i.e people running away) were considered as anomalies in the context of this work. The results have shown that the proposed method is well-comparable with other methods in the literature while being less complex and able to run in real time.

Research paper thumbnail of An unsupervised method for anomaly detection from crowd videos

2013 21st Signal Processing and Communications Applications Conference (SIU), 2013

Özetçe-Kalabalıkta aykırılık tespiti, kalabalık yerlerde toplum güvenliğini sağlamanın zorluğunda... more Özetçe-Kalabalıkta aykırılık tespiti, kalabalık yerlerde toplum güvenliğini sağlamanın zorluğundan ötürü önem kazanmaya başlayan bir konudur. Gözetleme videolarından kalabalık alanlardaki olayların gerçek zamanlı yakalanması, aykırılıkların tespiti açısından önemli bir yere sahiptir. Bu bildiride, kalabalıktaki aykırılıkları bilgisayarla görme ve otomatik öğrenme tekniklerini kullanarak gerçek zamanlı olarak tespit eden bir yöntem önerilmiştir. Önerilen yöntem, Ölçekten Bağımsız Öznitelik Dönüşümü (SIFT) öznitelik noktalarını takip ederek kalabalık hareketinin özelliklerini (hız, yön) çıkarmayı ve çıkarılan davranış özelliklerini Gauss tabanlı bir modele oturtmayı kapsamaktadır. Bu makalede, video karesinin bütününde oluşan bütünsel aykırılıklar tespit edilmiştir. Test sonuçlarına göre, önerilen yöntem en gelişmiş yöntemlerle karşılaştırılabilir sonuçlar vermektedir ve gerçek zamanlı çalışabilmektedir. Ayrıca karşılaştırılan yöntemlerden daha az karmaşıktır ve öğreticisiz bir yöntemdir. Anahtar Kelimeler-kalabalık davranış analizi; video gözetleme uygulamaları; bilgisayarla görme.

Research paper thumbnail of What's in the container? Classifying object contents from vision and touch

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014

Robots operating in household environments need to interact with food containers of different typ... more 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.

Research paper thumbnail of Real-time multi-camera video analytics system on GPU

Journal of Real-Time Image Processing, 2013

Research paper thumbnail of Estimating deformability of objects using meshless shape matching

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017

Humans interact with deformable objects on a daily basis but this still represents a challenge fo... more Humans interact with deformable objects on a daily basis but this still represents a challenge for robots. To enable manipulation of and interaction with deformable objects, robots need to be able to extract and learn the deformability of objects both prior to and during the interaction. Physics-based models are commonly used to predict the physical properties of deformable objects and simulate their deformation accurately. The most popular simulation techniques are force-based models that need force measurements. In this paper, we explore the applicability of a geometry-based simulation method called meshless shape matching (MSM) for estimating the deformability of objects. The main advantages of MSM are its controllability and computational efficiency that make it popular in computer graphics to simulate complex interactions of multiple objects at the same time. Additionally, a useful feature of the MSM that differentiates it from other physics-based simulation is to be independen...

Research paper thumbnail of Learning Object Properties From Manipulation for Manipulation

The world contains objects with various properties rigid, granular, liquid, elastic or plastic. A... more The world contains objects with various properties rigid, granular, liquid, elastic or plastic. As humans, while interacting with the objects, we plan our manipulation by considering their properties. For instance, while holding a rigid object such as a brick, we adapt our grasp based on its centre of mass not to drop it. On the other hand while manipulating a deformable object, we may consider additional properties to the centre of mass such elasticity, brittleness etc. for grasp stability. Therefore, knowing object properties is an integral part of skilled manipulation of objects. For manipulating objects skillfully, robots should be able to predict the object properties as humans do. To predict the properties, interactions with objects are essential. These interactions give rise distinct sensory signals that contains information about the object properties. The signals coming from a single sensory modality may give ambiguous information or noisy measurements. Hence, by integratin...

Research paper thumbnail of Learning Object Properties From Manipulation for Manipulation

The world contains objects with various properties - rigid, granular, liquid, elastic or plastic.... more The world contains objects with various properties - rigid, granular, liquid, elastic or plastic. As humans, while interacting with the objects, we plan our manipulation by considering their proper ...

Research paper thumbnail of Learning Object Properties From Manipulation for Manipulation

The world contains objects with various properties rigid, granular, liquid, elastic or plastic. A... more The world contains objects with various properties rigid, granular, liquid, elastic or plastic. As humans, while interacting with the objects, we plan our manipulation by considering their properties. For instance, while holding a rigid object such as a brick, we adapt our grasp based on its centre of mass not to drop it. On the other hand while manipulating a deformable object, we may consider additional properties to the centre of mass such elasticity, brittleness etc. for grasp stability. Therefore, knowing object properties is an integral part of skilled manipulation of objects. For manipulating objects skillfully, robots should be able to predict the object properties as humans do. To predict the properties, interactions with objects are essential. These interactions give rise distinct sensory signals that contains information about the object properties. The signals coming from a single sensory modality may give ambiguous information or noisy measurements. Hence, by integratin...

Research paper thumbnail of Active Perception and Modeling of Deformable Surfaces using Gaussian Processes and Position-based Dynamics

Exploring and modeling heterogeneous elastic surfaces requires multiple interactions with the env... more Exploring and modeling heterogeneous elastic surfaces requires multiple interactions with the environment and a complex selection of physical material parameters. The most common approaches model deformable properties from sets of offline observations using computationally expensive force-based simulators. In this work we present an online probabilistic framework for autonomous estimation of a deformability distribution map of heterogeneous elastic surfaces from few physical interactions. The method takes advantage of Gaussian Processes for constructing a model of the environment geometry surrounding a robot. A fast Position-based Dynamics simulator uses focused environmental observations in order to model the elastic behavior of portions of the environment. Gaussian Process Regression maps the local deformability on the whole environment in order to generate a deformability distribution map. We show experimental results using a PrimeSense camera, a Kinova Jaco2 robotic arm and an O...

Research paper thumbnail of Modeling of Deformable Objects for Robotic Manipulation: A Tutorial and Review

Frontiers in Robotics and AI

Manipulation of deformable objects has given rise to an important set of open problems in the fie... more Manipulation of deformable objects has given rise to an important set of open problems in the field of robotics. Application areas include robotic surgery, household robotics, manufacturing, logistics, and agriculture, to name a few. Related research problems span modeling and estimation of an object's shape, estimation of an object's material properties, such as elasticity and plasticity, object tracking and state estimation during manipulation, and manipulation planning and control. In this survey article, we start by providing a tutorial on foundational aspects of models of shape and shape dynamics. We then use this as the basis for a review of existing work on learning and estimation of these models and on motion planning and control to achieve desired deformations. We also discuss potential future lines of work.

Research paper thumbnail of Active perception and modeling of deformable surfaces using Gaussian processes and position-based dynamics

2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), Nov 1, 2016

Exploring and modeling heterogeneous elastic surfaces requires multiple interactions with the env... more Exploring and modeling heterogeneous elastic surfaces requires multiple interactions with the environment and a complex selection of physical material parameters. The most common approaches model deformable properties from sets of offline observations using computationally expensive forcebased simulators. In this work we present an online probabilistic framework for autonomous estimation of a deformability distribution map of heterogeneous elastic surfaces from few physical interactions. The method takes advantage of Gaussian Processes for constructing a model of the environment geometry surrounding a robot. A fast Position-based Dynamics simulator uses focused environmental observations in order to model the elastic behavior of portions of the environment. Gaussian Process Regression maps the local deformability on the whole environment in order to generate a deformability distribution map. We show experimental results using a PrimeSense camera, a Kinova Jaco2 robotic arm and an Optoforce sensor on different deformable surfaces.

Research paper thumbnail of Real-Time Global Anomaly Detection for Crowd Video Surveillance Using SIFT

5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013), 2013

Automated analysis of crowd behaviour using surveillance videos is an important issue for public ... more Automated analysis of crowd behaviour using surveillance videos is an important issue for public security as it allows detection of potentially dangerous situations in crowds. Although there is a considerable amount of study in crowd behaviour analysis, the majority are limited in several ways. A few problems to mention are: limited real-time considerations, requirement of pre-set rigid anomaly rules, and high algorithm complexity. In this paper, we propose a Scale Invariant Feature Transform (SIFT) based holistic approach which is able to run in real time to detect global anomalies. Events which deviate significantly from the normal behaviour in the data set (i.e people running away) were considered as anomalies in the context of this work. The results have shown that the proposed method is well-comparable with other methods in the literature while being less complex and able to run in real time.

Research paper thumbnail of An unsupervised method for anomaly detection from crowd videos

2013 21st Signal Processing and Communications Applications Conference (SIU), 2013

Özetçe-Kalabalıkta aykırılık tespiti, kalabalık yerlerde toplum güvenliğini sağlamanın zorluğunda... more Özetçe-Kalabalıkta aykırılık tespiti, kalabalık yerlerde toplum güvenliğini sağlamanın zorluğundan ötürü önem kazanmaya başlayan bir konudur. Gözetleme videolarından kalabalık alanlardaki olayların gerçek zamanlı yakalanması, aykırılıkların tespiti açısından önemli bir yere sahiptir. Bu bildiride, kalabalıktaki aykırılıkları bilgisayarla görme ve otomatik öğrenme tekniklerini kullanarak gerçek zamanlı olarak tespit eden bir yöntem önerilmiştir. Önerilen yöntem, Ölçekten Bağımsız Öznitelik Dönüşümü (SIFT) öznitelik noktalarını takip ederek kalabalık hareketinin özelliklerini (hız, yön) çıkarmayı ve çıkarılan davranış özelliklerini Gauss tabanlı bir modele oturtmayı kapsamaktadır. Bu makalede, video karesinin bütününde oluşan bütünsel aykırılıklar tespit edilmiştir. Test sonuçlarına göre, önerilen yöntem en gelişmiş yöntemlerle karşılaştırılabilir sonuçlar vermektedir ve gerçek zamanlı çalışabilmektedir. Ayrıca karşılaştırılan yöntemlerden daha az karmaşıktır ve öğreticisiz bir yöntemdir. Anahtar Kelimeler-kalabalık davranış analizi; video gözetleme uygulamaları; bilgisayarla görme.

Research paper thumbnail of What's in the container? Classifying object contents from vision and touch

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014

Robots operating in household environments need to interact with food containers of different typ... more 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.

Research paper thumbnail of Real-time multi-camera video analytics system on GPU

Journal of Real-Time Image Processing, 2013

Research paper thumbnail of Estimating deformability of objects using meshless shape matching

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017

Humans interact with deformable objects on a daily basis but this still represents a challenge fo... more Humans interact with deformable objects on a daily basis but this still represents a challenge for robots. To enable manipulation of and interaction with deformable objects, robots need to be able to extract and learn the deformability of objects both prior to and during the interaction. Physics-based models are commonly used to predict the physical properties of deformable objects and simulate their deformation accurately. The most popular simulation techniques are force-based models that need force measurements. In this paper, we explore the applicability of a geometry-based simulation method called meshless shape matching (MSM) for estimating the deformability of objects. The main advantages of MSM are its controllability and computational efficiency that make it popular in computer graphics to simulate complex interactions of multiple objects at the same time. Additionally, a useful feature of the MSM that differentiates it from other physics-based simulation is to be independen...

Research paper thumbnail of Learning Object Properties From Manipulation for Manipulation

The world contains objects with various properties rigid, granular, liquid, elastic or plastic. A... more The world contains objects with various properties rigid, granular, liquid, elastic or plastic. As humans, while interacting with the objects, we plan our manipulation by considering their properties. For instance, while holding a rigid object such as a brick, we adapt our grasp based on its centre of mass not to drop it. On the other hand while manipulating a deformable object, we may consider additional properties to the centre of mass such elasticity, brittleness etc. for grasp stability. Therefore, knowing object properties is an integral part of skilled manipulation of objects. For manipulating objects skillfully, robots should be able to predict the object properties as humans do. To predict the properties, interactions with objects are essential. These interactions give rise distinct sensory signals that contains information about the object properties. The signals coming from a single sensory modality may give ambiguous information or noisy measurements. Hence, by integratin...

Research paper thumbnail of Learning Object Properties From Manipulation for Manipulation

The world contains objects with various properties - rigid, granular, liquid, elastic or plastic.... more The world contains objects with various properties - rigid, granular, liquid, elastic or plastic. As humans, while interacting with the objects, we plan our manipulation by considering their proper ...

Research paper thumbnail of Learning Object Properties From Manipulation for Manipulation

The world contains objects with various properties rigid, granular, liquid, elastic or plastic. A... more The world contains objects with various properties rigid, granular, liquid, elastic or plastic. As humans, while interacting with the objects, we plan our manipulation by considering their properties. For instance, while holding a rigid object such as a brick, we adapt our grasp based on its centre of mass not to drop it. On the other hand while manipulating a deformable object, we may consider additional properties to the centre of mass such elasticity, brittleness etc. for grasp stability. Therefore, knowing object properties is an integral part of skilled manipulation of objects. For manipulating objects skillfully, robots should be able to predict the object properties as humans do. To predict the properties, interactions with objects are essential. These interactions give rise distinct sensory signals that contains information about the object properties. The signals coming from a single sensory modality may give ambiguous information or noisy measurements. Hence, by integratin...