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Vision Based Robotic Grasping From Real Time 6 Dof Deep Object Pose Estimation To Deep Grasp Estimation For Parallel Grippers
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Book Synopsis Vision-based Robotic Grasping from Real-time 6-DoF Deep Object Pose Estimation to Deep Grasp Estimation for Parallel Grippers by : Nico Leuze
Download or read book Vision-based Robotic Grasping from Real-time 6-DoF Deep Object Pose Estimation to Deep Grasp Estimation for Parallel Grippers written by Nico Leuze and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Touch Based Object Pose Estimation for Robotic Grasping by : Joao Bimbo
Download or read book Touch Based Object Pose Estimation for Robotic Grasping written by Joao Bimbo and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robot grasping and manipulation require very accurate and timely knowledge of the manipulated object's shape and pose to successfully perform a desired task. One of the main reasons current systems fail to carry out complex tasks in a real, unstructured environment is their inability to accurately determine where in, the object the fingers are touching. Most systems use vision to detect the pose of an object, but the performance of this sensing modality deteriorates as soon as the robot grasps the object. When the robot hand contacts an object, it partially occludes it, which makes it difficult for vision systems to track the object's location. This thesis presents algorithms to use the robot's available tactile sensing to correct the visually determined pose of a grasped object. This method is extended to globally estimate the pose of the object even when no initial estimate is given. Two different tactile sensing strategies have been employed: single-point and distributed, and measurement models for these two strategies are presented. Different optimisation algorithms are developed and tested to minimise the output of these measurement models and find one or more poses that satisfy current tactile measurements. Results show that the method is able to successfully estimate the pose of a grasped object with high accuracy, even for objects with a high degree of geometric complexity. Other applications of the method are proposed, such as determining grasp stability or identifying the grasped object, as well as future research directions.
Book Synopsis Robotic Object Pose Estimation with Deep Neural Networks by : Jimmy Wu (M. Eng.)
Download or read book Robotic Object Pose Estimation with Deep Neural Networks written by Jimmy Wu (M. Eng.) and published by . This book was released on 2018 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, we introduce pose interpreter networks for 6-DoF object pose estimation. In contrast to other CNN-based approaches to pose estimation that require expensively-annotated object pose data, our pose interpreter network is trained entirely on synthetic data. We use object masks as an intermediate representation to bridge real and synthetic. We show that when combined with a segmentation model trained on RGB images, our synthetically-trained pose interpreter network is able to generalize to real data. Our end-to-end system for object pose estimation runs in real-time (20 Hz) on live RGB data, without using depth information or ICP refinement.
Book Synopsis Aerial Manipulation by : Matko Orsag
Download or read book Aerial Manipulation written by Matko Orsag and published by Springer. This book was released on 2017-09-19 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text is a thorough treatment of the rapidly growing area of aerial manipulation. It details all the design steps required for the modeling and control of unmanned aerial vehicles (UAV) equipped with robotic manipulators. Starting with the physical basics of rigid-body kinematics, the book gives an in-depth presentation of local and global coordinates, together with the representation of orientation and motion in fixed- and moving-coordinate systems. Coverage of the kinematics and dynamics of unmanned aerial vehicles is developed in a succession of popular UAV configurations for multirotor systems. Such an arrangement, supported by frequent examples and end-of-chapter exercises, leads the reader from simple to more complex UAV configurations. Propulsion-system aerodynamics, essential in UAV design, is analyzed through blade-element and momentum theories, analysis which is followed by a description of drag and ground-aerodynamic effects. The central part of the book is dedicated to aerial-manipulator kinematics, dynamics, and control. Based on foundations laid in the opening chapters, this portion of the book is a structured presentation of Newton–Euler dynamic modeling that results in forward and backward equations in both fixed- and moving-coordinate systems. The Lagrange–Euler approach is applied to expand the model further, providing formalisms to model the variable moment of inertia later used to analyze the dynamics of aerial manipulators in contact with the environment. Using knowledge from sensor data, insights are presented into the ways in which linear, robust, and adaptive control techniques can be applied in aerial manipulation so as to tackle the real-world problems faced by scholars and engineers in the design and implementation of aerial robotics systems. The book is completed by path and trajectory planning with vision-based examples for tracking and manipulation.
Book Synopsis Robotic Grasping and Manipulation by : Yu Sun
Download or read book Robotic Grasping and Manipulation written by Yu Sun and published by Springer. This book was released on 2018-07-15 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First Robotic Grasping and Manipulation Challenge, RGMC 2016, held at IROS 2016, Daejeon, South Korea, in October 2016.The 13 revised full papers presented were carefully reviewed and are describing the rules, results, competitor systems and future directions of the inaugural competition. The competition was designed to allow researchers focused on the application of robot systems to compare the performance of hand designs as well as autonomous grasping and manipulation solutions across a common set of tasks. The competition was comprised of three tracks that included hand-in-hand grasping, fully autonomous grasping, and simulation.
Book Synopsis Springer Handbook of Automation by : Shimon Y. Nof
Download or read book Springer Handbook of Automation written by Shimon Y. Nof and published by Springer Nature. This book was released on 2023-06-16 with total page 1533 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook incorporates new developments in automation. It also presents a widespread and well-structured conglomeration of new emerging application areas, such as medical systems and health, transportation, security and maintenance, service, construction and retail as well as production or logistics. The handbook is not only an ideal resource for automation experts but also for people new to this expanding field.
Book Synopsis Deep Learning-based Approaches for Depth and 6-DoF Pose Estimation by : Muyuan Lin (S.M.)
Download or read book Deep Learning-based Approaches for Depth and 6-DoF Pose Estimation written by Muyuan Lin (S.M.) and published by . This book was released on 2020 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we investigated two important geometric vision problems, namely, depth estimation from a single RGB image, and 6-DoF object pose estimation from a partial point cloud. Geometric vision problems are concerned with extracting information (e.g. depth, agent trajectory, 3D structure, 6-DoF pose of objects) of the scene from noisy sensor data (e.g. RGB images, LiDAR) by exploiting geometric constraints (e.g. epipolar constraint, rigid motion of objects). Deep learning framework has achieved impressive progress in many computer vision tasks such as image recognition and segmentation. However, applying deep learning-based approaches to geometric vision problems, which are particularly important in safety-critical robotics applications, remains an open problem. The main challenge lies in the fact that it is not straightforward to incorporate geometric constraints, arising from image formation process and physical properties, to optimization problems. To this end, we explore possibilities of enforcing such constraints either by decomposing a problem into two sub-problems each respecting desired constraints, or designing an estimator establishing relationship between intermediate representations and predicted outputs. We propose a deep learning-based approach for -each problem. Through extensive experiments, we show that our proposed approaches produce results comparable with state of the art on public datasets.
Book Synopsis Scene Reconstruction for Simulated Grasp Search in Structured Clutter by : Runlin Guo
Download or read book Scene Reconstruction for Simulated Grasp Search in Structured Clutter written by Runlin Guo and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robotic grasping in a complex environment is one of the fundamental challenges for home-assistant robots. Complex environment grasping has been extensively studied in industrial bin-picking scenarios, where reliably grasping objects from unorganized heaps is challenging due to sensor noise, obstructions, and occlusions. However, bin picking is still relatively easier than grasping common household objects from a structured clutter in a home environment because the robot cannot knock over neighboring objects during the grasping motion. Recently, there have been several attempts to tackle the grasping-in-structured-clutter problem. In our experiments, we found these methods either hard to adapt to our simulated environment without extra tuning or generate too few stable grasps to successfully grasp the objects. The overviews and detailed analyses of these existing grasping approaches will appear in the first half of this thesis. In the second half of this thesis, we investigate the idea of using a physical simulator as an intermediate step to generate a grasp trajectory proposal. At a high level, we propose a two-step approach to solve the grasping-in-structured-clutter problem. First, we collect RGB-D observations to reconstruct the environment in a physical simulator via 9 degree-of-freedom (DoF) category-level object pose estimation, CAD model matching, and physical support refinement. Then, we perform antipodal grasp sampling, collision-free motion planning, and grasp execution in the simulator and directly transfer the robot arm's motion trajectory to the original environment. To generate a 9-DoF category-level object pose estimate, we extend a state-of-the-art 6-DoF instance-level object pose estimation network. In our experiments, we found the 9-DoF pose estimation network can reach performance comparable to the state-of-the-art on a category-level object pose estimation dataset. Relying on only the top-down view of the environment, we reconstructed the environment using the proposed two-step approach and evaluated the grasp transfer success. The results show further room for improvements in the model matching process. Future directions and some ideas will be discussed towards the end of this thesis. We hope the work of scene reconstruction for simulated grasp search and trajectory transfer will help future research of robotics manipulation in complex environments.
Book Synopsis Estimation and Inference for Grasping and Manipulation Tasks Using Vision and Kinesthetic Sensors by : Paul Hebert
Download or read book Estimation and Inference for Grasping and Manipulation Tasks Using Vision and Kinesthetic Sensors written by Paul Hebert and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents a novel framework for state estimation in the context of robotic grasping and manipulation. The overall estimation approach is based on fusing various visual cues for manipulator tracking, namely appearance and feature-based, shape-based, and silhouette-based visual cues. Similarly, a framework is developed to fuse the above visual cues, but also kinesthetic cues such as force-torque and tactile measurements, for in-hand object pose estimation. The cues are extracted from multiple sensor modalities and are fused in a variety of Kalman filters. A hybrid estimator is developed to estimate both a continuous state (robot and object states) and discrete states, called contact modes, which specify how each finger contacts a particular object surface. A static multiple model estimator is used to compute and maintain this mode probability. The thesis also develops an estimation framework for estimating model parameters associated with object grasping. Dual and joint state-parameter estimation is explored for parameter estimation of a grasped object's mass and center of mass. Experimental results demonstrate simultaneous object localization and center of mass estimation. Dual-arm estimation is developed for two arm robotic manipulation tasks. Two types of filters are explored; the first is an augmented filter that contains both arms in the state vector while the second runs two filters in parallel, one for each arm. These two frameworks and their performance is compared in a dual-arm task of removing a wheel from a hub. This thesis also presents a new method for action selection involving touch. This next best touch method selects an available action for interacting with an object that will gain the most information. The algorithm employs information theory to compute an information gain metric that is based on a probabilistic belief suitable for the task. An estimation framework is used to maintain this belief over time. Kinesthetic measurements such as contact and tactile measurements are used to update the state belief after every interactive action. Simulation and experimental results are demonstrated using next best touch for object localization, specifically a door handle on a door. The next best touch theory is extended for model parameter determination. Since many objects within a particular object category share the same rough shape, principle component analysis may be used to parametrize the object mesh models. These parameters can be estimated using the action selection technique that selects the touching action which best both localizes and estimates these parameters. Simulation results are then presented involving localizing and determining a parameter of a screwdriver. Lastly, the next best touch theory is further extended to model classes. Instead of estimating parameters, object class determination is incorporated into the information gain metric calculation. The best touching action is selected in order to best discern between the possible model classes. Simulation results are presented to validate the theory.
Book Synopsis Springer Handbook of Robotics by : Bruno Siciliano
Download or read book Springer Handbook of Robotics written by Bruno Siciliano and published by Springer. This book was released on 2016-07-27 with total page 2259 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of this handbook provides a state-of-the-art overview on the various aspects in the rapidly developing field of robotics. Reaching for the human frontier, robotics is vigorously engaged in the growing challenges of new emerging domains. Interacting, exploring, and working with humans, the new generation of robots will increasingly touch people and their lives. The credible prospect of practical robots among humans is the result of the scientific endeavour of a half a century of robotic developments that established robotics as a modern scientific discipline. The ongoing vibrant expansion and strong growth of the field during the last decade has fueled this second edition of the Springer Handbook of Robotics. The first edition of the handbook soon became a landmark in robotics publishing and won the American Association of Publishers PROSE Award for Excellence in Physical Sciences & Mathematics as well as the organization’s Award for Engineering & Technology. The second edition of the handbook, edited by two internationally renowned scientists with the support of an outstanding team of seven part editors and more than 200 authors, continues to be an authoritative reference for robotics researchers, newcomers to the field, and scholars from related disciplines. The contents have been restructured to achieve four main objectives: the enlargement of foundational topics for robotics, the enlightenment of design of various types of robotic systems, the extension of the treatment on robots moving in the environment, and the enrichment of advanced robotics applications. Further to an extensive update, fifteen new chapters have been introduced on emerging topics, and a new generation of authors have joined the handbook’s team. A novel addition to the second edition is a comprehensive collection of multimedia references to more than 700 videos, which bring valuable insight into the contents. The videos can be viewed directly augmented into the text with a smartphone or tablet using a unique and specially designed app. Springer Handbook of Robotics Multimedia Extension Portal: http://handbookofrobotics.org/
Book Synopsis Antipodal Robotic Grasping Using Deep Learning by : Shirin Joshi
Download or read book Antipodal Robotic Grasping Using Deep Learning written by Shirin Joshi and published by . This book was released on 2020 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: "In this work, we discuss two implementations that predict antipodal grasps for novel objects: A deep Q-learning approach and a Generative Residual Convolutional Neural Network approach. We present a deep reinforcement learning based method to solve the problem of robotic grasping using visio-motor feedback. The use of a deep learning based approach reduces the complexity caused by the use of hand-designed features. Our method uses an off-policy reinforcement learning framework to learn the grasping policy. We use the double deep Q-learning framework along with a novel Grasp-Q-Network to output grasp probabilities used to learn grasps that maximize the pick success. We propose a visual servoing mechanism that uses a multi-view camera setup that observes the scene which contains the objects of interest. We performed experiments using a Baxter Gazebo simulated environment as well as on the actual robot. The results show that our proposed method outperforms the baseline Q-learning framework and increases grasping accuracy by adapting a multi-view model in comparison to a single-view model. The second method tackles the problem of generating antipodal robotic grasps for unknown objects from an n-channel image of the scene. We propose a novel Generative Residual Convolutional Neural Network (GR-ConvNet) model that can generate robust antipodal grasps from n-channel input at real-time speeds (20ms). We evaluate the proposed model architecture on standard dataset and previously unseen household objects. We achieved state-of-the-art accuracy of 97.7% on Cornell grasp dataset. We also demonstrate a 93.5% grasp success rate on previously unseen real-world objects."--Abstract.
Book Synopsis Robotic Grasping Using Demonstration and Deep Learning by : Victor Reyes Osorio
Download or read book Robotic Grasping Using Demonstration and Deep Learning written by Victor Reyes Osorio and published by . This book was released on 2019 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robotic grasping is a challenging task that has been approached in a variety of ways. Historically grasping has been approached as a control problem. If the forces between the robotic gripper and the object can be calculated and controlled accurately then grasps can be easily planned. However, these methods are difficult to extend to unknown objects or a variety of robotic grippers. Using human demonstrated grasps is another way to tackle this problem. Under this approach, a human operator guides the robot in a training phase to perform the grasping task and then the useful information from each demonstration is extracted. Unlike traditional control systems, demonstration based systems do not explicitly state what forces are necessary, and they also allow the system to learn to manipulate the robot directly. However, the major failing of this approach is the sheer amount of data that would be required to present a demonstration for a substantial portion of objects and use cases. Recently, we have seen various deep learning grasping systems that achieve impressive levels of performance. These systems learn to map perceptual features, like color images and depth maps, to gripper poses. These systems can learn complicated relationships, but still require massive amounts of data to train properly. A common way of collecting this data is to run physics based simulations based on the control schemes mentioned above, however human demonstrated grasps are still the gold standard for grasp planning. We therefore propose a data collection system that can be used to collect a large number of human demonstrated grasps. In this system the human demonstrator holds the robotic gripper in one hand and naturally uses the gripper to perform grasps. These grasp poses are tracked fully in six dimensions and RGB-D images are collected for each grasp trial showing the object and any obstacles present during the grasp trial. Implementing this system, we collected 40K annotated grasps demonstrations. This dataset is available online. We test a subset of these grasps for their robustness to perturbations by replicating scenes captured during data collection and using a robotic arm to replicate the grasps we collected. We find that we can replicate the scenes with low variance, which coupled with the robotic arm's low repeatability error means that we can test a wide variety of perturbations. Our tests show that our grasps can maintain a probability of success over 90% for perturbations of up 2.5cm or 10 degrees. We then train a variety of neural networks to learn to map images of grasping scenes to final grasp poses. We separate the task of pose prediction into two separate networks: a network to predict the position of the gripper, and a network to predict the orientation conditioned on the output of the position network. These networks are trained to classify whether a particular position or orientation is likely to lead to a successful grasp. We also identified a strong prior in our dataset over the distribution of grasp positions and leverage this information by tasking the position network to predict corrections to this prior based on the image being presented to it. Our final network architecture, using layers from a pre-trained state of the art image classification network and residual convolution blocks, did not seem able to learn the grasping task. We observed a strong tendency for the networks to overfit, even when the networks had been heavily regularized and parameters reduced substantially. The best position network we were able to train collapses to only predicting a few possible positions, leading to the orientation network to only predict a few possible orientations as well. Limited testing on a robotic platform confirmed these findings.
Book Synopsis Learning to Interact with Environment Via Geometry-Based Robot Grasping by : Yuzhe Qin
Download or read book Learning to Interact with Environment Via Geometry-Based Robot Grasping written by Yuzhe Qin and published by . This book was released on 2020 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ability to learning from interaction with environments shapes an intelligent agent. For exploratory robots, they need specific structured action to interact with the physical world efficiently. Geometry-based grasping, which serves as the primary action for many complex manipulation tasks, can be of great help for robot exploration. With a learned grasping strategy, the robot can directly execute object-specific action. This thesis studies the problem of 6-DoF geometric grasping by a parallel gripper captured using a commodity depth sensor from a single viewpoint. We address the problem in a learning-based framework with point cloud input. At the higher level, we rely on a single-shot grasp proposal network built upon the PointNet++ backbone. Our single-shot neural network architecture can predict grasp proposals efficiently and effectively. At the lower level, we proposed a method to generate training data automatically. Our training data synthesis pipeline can generate scenes of complex object configuration and leverage an innovative gripper contact model to create dense and high-quality grasp annotations. Experiments in synthetic and real environments have demonstrated that the proposed approach can outperform the state-of-the-art geometry-based grasping method by a large margin. The grasp proposal network trained in a synthetic scene can work well in real-world scenarios, which also shows the point-based method have high potential to bridge the sim-to-real gap. We hope the work of the geometric grasping algorithm will help future research for more complex robot manipulation skills.
Book Synopsis The Human Hand as an Inspiration for Robot Hand Development by : Ravi Balasubramanian
Download or read book The Human Hand as an Inspiration for Robot Hand Development written by Ravi Balasubramanian and published by Springer. This book was released on 2014-01-03 with total page 573 pages. Available in PDF, EPUB and Kindle. Book excerpt: “The Human Hand as an Inspiration for Robot Hand Development” presents an edited collection of authoritative contributions in the area of robot hands. The results described in the volume are expected to lead to more robust, dependable, and inexpensive distributed systems such as those endowed with complex and advanced sensing, actuation, computation, and communication capabilities. The twenty-four chapters discuss the field of robotic grasping and manipulation viewed in light of the human hand’s capabilities and push the state-of-the-art in robot hand design and control. Topics discussed include human hand biomechanics, neural control, sensory feedback and perception, and robotic grasp and manipulation. This book will be useful for researchers from diverse areas such as robotics, biomechanics, neuroscience, and anthropologists.
Book Synopsis Statistics of Directional Data by : K. V. Mardia
Download or read book Statistics of Directional Data written by K. V. Mardia and published by Academic Press. This book was released on 2014-07-03 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability and Mathematical Statistics: A Series of Monographs and Textbooks: Statistics of Directional Data aims to provide a systematic account of statistical theory and methodology for observations which are directions. The publication first elaborates on angular data and frequency distributions, descriptive measures, and basic concepts and theoretical models. Discussions focus on moments and measures of location and dispersion, distribution function, corrections for grouping, calculation of the mean direction and the circular variance, interrelations between different units of angular measurement, and diagrammatical representation. The book then examines fundamental theorems and distribution theory, point estimation, and tests for samples from von Mises populations. The text takes a look at non-parametric tests, distributions on spheres, and inference problems on the sphere. Topics include tests for axial data, point estimation, distribution theory, moments and limiting distributions, and tests of goodness of fit and tests of uniformity. The publication is a dependable reference for researchers interested in probability and mathematical statistics.
Book Synopsis Geometry-based Object Pose Estimation and Grasp Detection for Industrial Robotic Random Picking Systems Equipped with a 3D Vision Sensor by : 王喻民
Download or read book Geometry-based Object Pose Estimation and Grasp Detection for Industrial Robotic Random Picking Systems Equipped with a 3D Vision Sensor written by 王喻民 and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Underactuated Robotic Hands by : Lionel Birglen
Download or read book Underactuated Robotic Hands written by Lionel Birglen and published by Springer Science & Business Media. This book was released on 2008-02-11 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a cornerstone publication in robotic grasping. The authors have developed an internationally recognized expertise in this area. Additionally, they designed and built several prototypes which attracted the attention of the scientific community. The purpose of this book is to summarize years of research and to present, in an attractive format, the expertise developed by the authors on a new technology for grasping which has achieved great success both in theory and in practice.
