Visuo Tactile Perception For Dexterous Robotic Manipulation PDF Download
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| Author | : Maria Bauza Villalonga |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : |
| ISBN | : |
Download Visuo-tactile Perception for Dexterous Robotic Manipulation Book in PDF, ePub and Kindle
In this thesis, we develop visuo-tactile perception to enable general and precise robotic manipulation. In particular, we want to study how to effectively process visual and tactile information to allow robots to expand their capabilities while remaining accurate and reliable. We begin our work by focusing on developing tools for tactile perception. For the task of grasping, we use tactile observations to assess and improve grasp stability. Tactile information also allows extracting geometric information from contacts which is a task-independent feature. By learning to map tactile observations to contact shapes, we show robots can reconstruct accurate 3D models of objects, which can later be used for pose estimation. We build on the idea of using geometric information from contacts by developing tools that accurately render contact geometry in simulation. This enables us to develop a probabilistic approach to pose estimation for novel objects based on matching real visuo-tactile observations to a set of simulated ones. As a result, our method does not rely on real data and yields accurate pose distributions. Finally, we demonstrate how this approach to perception enables precise manipulations. In particular, we consider the task of precise pick-and-place of novel objects. Combining perception with task-aware planning, we build a robotic system that identifies in simulation which object grasps will facilitate grasping, planning, and perception; and selects the best one during execution. Our approach adapts to new objects by learning object-dependent models purely in simulation, allowing a robot to manipulate new objects successfully and perform highly accurate placements.
| Author | : Qiang Li |
| Publisher | : Academic Press |
| Total Pages | : 374 |
| Release | : 2022-04-02 |
| Genre | : Computers |
| ISBN | : 0323904173 |
Download Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation Book in PDF, ePub and Kindle
Tactile Sensing, Skill Learning and Robotic Dexterous Manipulation focuses on cross-disciplinary lines of research and groundbreaking research ideas in three research lines: tactile sensing, skill learning and dexterous control. The book introduces recent work about human dexterous skill representation and learning, along with discussions of tactile sensing and its applications on unknown objects’ property recognition and reconstruction. Sections also introduce the adaptive control schema and its learning by imitation and exploration. Other chapters describe the fundamental part of relevant research, paying attention to the connection among different fields and showing the state-of-the-art in related branches. The book summarizes the different approaches and discusses the pros and cons of each. Chapters not only describe the research but also include basic knowledge that can help readers understand the proposed work, making it an excellent resource for researchers and professionals who work in the robotics industry, haptics and in machine learning. Provides a review of tactile perception and the latest advances in the use of robotic dexterous manipulation Presents the most detailed work on synthesizing intelligent tactile perception, skill learning and adaptive control Introduces recent work on human’s dexterous skill representation and learning and the adaptive control schema and its learning by imitation and exploration Reveals and illustrates how robots can improve dexterity by modern tactile sensing, interactive perception, learning and adaptive control approaches
| Author | : Tetsuyou Watanabe |
| Publisher | : Academic Press |
| Total Pages | : 0 |
| Release | : 2018-06-29 |
| Genre | : Technology & Engineering |
| ISBN | : 9780128133859 |
Download Human Inspired Dexterity in Robotic Manipulation Book in PDF, ePub and Kindle
Human Inspired Dexterity in Robotic Manipulation provides up-to-date research and information on how to imitate humans and realize robotic manipulation. Approaches from both software and hardware viewpoints are shown, with sections discussing, and highlighting, case studies that demonstrate how human manipulation techniques or skills can be transferred to robotic manipulation. From the hardware viewpoint, the book discusses important human hand structures that are key for robotic hand design and how they should be embedded for dexterous manipulation. This book is ideal for the research communities in robotics, mechatronics and automation.
| Author | : Geoffrey Taylor |
| Publisher | : Springer |
| Total Pages | : 231 |
| Release | : 2008-08-18 |
| Genre | : Technology & Engineering |
| ISBN | : 3540334556 |
Download Visual Perception and Robotic Manipulation Book in PDF, ePub and Kindle
This book moves toward the realization of domestic robots by presenting an integrated view of computer vision and robotics, covering fundamental topics including optimal sensor design, visual servo-ing, 3D object modelling and recognition, and multi-cue tracking, emphasizing robustness throughout. Covering theory and implementation, experimental results and comprehensive multimedia support including video clips, VRML data, C++ code and lecture slides, this book is a practical reference for roboticists and a valuable teaching resource.
| Author | : Kenneth Gutierrez |
| Publisher | : |
| Total Pages | : 119 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
Download Model-free Approaches to Robotic Manipulation Via Tactile Perception and Tension-driven Control Book in PDF, ePub and Kindle
To execute manipulation tasks in unstructured environments, robots use computer vision and a priori information to locate and grasp objects of interest. However, once an object has been grasped, cameras cannot perceive tactile- or force-based information about finger-object interactions. To address this, tactile and proprioception data are used to develop novel methodologies that aid in robotic manipulation once an object has been grasped. In the first study, a method was developed for the perception of tactile directionality using convolutional neural networks (CNNs). The deformation of a tactile sensor is used to perceive the direction of a tangential stimulus acting on the fingerpad. A primary CNN was used to estimate the direction of perturbations applied to a grasped object. A secondary CNN provided a measure of uncertainty through the use of confidence intervals. Our CNN models were able to perceive tactile directionality on par with humans, outperformed a state-of-the-art force estimator network, and was demonstrated in real-time. In the second study, novel controllers were developed for model-free, tension-driven manipulation of deformable linear objects (DLOs) using force-based data. Prior works on DLO manipulation have focused on geometric or topological state and used complex modeling and computer vision approaches. In tasks such as wrapping a DLO around a structure, DLO tension needs to be carefully controlled. Such tension control cannot be achieved using vision alone once the DLO becomes taut. Two controllers were designed to regulate the tension of a DLO and precede traditional motion controllers. The controllers could be used for tasks in which maintaining DLO tension takes higher priority over exact DLO configuration. We evaluate and demonstrate the controllers in real-time on real robots for two different utilitarian tasks: circular wrapping around a horizontal post and figure-eight wrapping around a boat cleat. In summary, methods were developed to effectively manipulate objects using tactile- and force-based information. The model-free nature of the approaches allows the techniques to be utilized without exact knowledge of object properties. Our methods that leverage tactile sensation and proprioception for object manipulation can serve as a foundation for further enhancement with complementary sensory feedback such as computer vision.
| Author | : Huaping Liu |
| Publisher | : Springer |
| Total Pages | : 220 |
| Release | : 2018-03-20 |
| Genre | : Computers |
| ISBN | : 9811061718 |
Download Robotic Tactile Perception and Understanding Book in PDF, ePub and Kindle
This book introduces the challenges of robotic tactile perception and task understanding, and describes an advanced approach based on machine learning and sparse coding techniques. Further, a set of structured sparse coding models is developed to address the issues of dynamic tactile sensing. The book then proves that the proposed framework is effective in solving the problems of multi-finger tactile object recognition, multi-label tactile adjective recognition and multi-category material analysis, which are all challenging practical problems in the fields of robotics and automation. The proposed sparse coding model can be used to tackle the challenging visual-tactile fusion recognition problem, and the book develops a series of efficient optimization algorithms to implement the model. It is suitable as a reference book for graduate students with a basic knowledge of machine learning as well as professional researchers interested in robotic tactile perception and understanding, and machine learning.
| Author | : Francois R. Hogan |
| Publisher | : |
| Total Pages | : 120 |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
Download Reactive Manipulation with Contact Models and Tactile Feedback Book in PDF, ePub and Kindle
This thesis focuses on closing the loop in robotic manipulation, moving towards robots that can better perceive their environment and react to unforeseen situations. Humans effectively process and react to information from visual and tactile sensing, however robots often remain programmed in an open-loop fashion, and struggle to correct their motion based on detected errors. We begin our work by developing full-state feedback controllers for dynamical systems involving frictional contact interactions. Hybridness and underactuation are key characteristics of these systems that complicate the design of feedback controllers. We design and experimentally validate the controllers on a planar manipulation system where the purpose is to control the motion of a sliding object on a flat surface using a point robotic pusher. The pusher-slider is a simple dynamical system that retains many of the challenges that are typical of robotic manipulation tasks. We extend this work to partially observable systems, by developing closed-loop tactile controllers for dexterous manipulation with dual-arm robotic palms. We introduce Tactile Dexterity, an approach to dexterous manipulation that plans for robot/object interactions that render interpretable tactile information for control. Key to this formulation is the decomposition of manipulation plans into sequences of manipulation primitives with simple mechanics and efficient planners.
| Author | : Lin, Xi |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
Download Robotic Touch for Contact Perception Book in PDF, ePub and Kindle
Tactile perception subserves the impressive dexterity found in humans but also found in their robotic counterparts. Recently, a new wave of tactile sensors relying on off-the-shelf cameras, provide a dense tactile image of the contact. However, by the way these sensors operate, the link between the mechanics of the skin and the tactile images is not evident. In this thesis, we present a novel camera-based tactile sensor, named ChromaTouch, which captures physically-driven dense images of the three-dimensional interaction that happens at the interface between the artificial skin and the touched object. The sensor measures the strain field induced by the contact, by imaging the pattern and color change of two overlapping markers array, one translucent and yellow and the other opaque and magenta. The motif seen by the camera is a bijective function of the relative motion of the markers allowing a reconstruction of the stress and strain field at the interface. The sensor, boasting up to 441 sensing elements, shows high robustness to external luminosity and camera resolution, and it is able to estimate the local coefficient of friction of the contact surface with one simple press. A hemispherical version extended the results to arbitrary shapes and is able to estimate the local curvature via a simple press using Hertz contact theory. Sensing the dense 3d deformation field at the contact opens the doors to a comprehensive, physically-based measurement of the interaction. Improved artificial perception of the object and of the interaction can inform robotic exploration, dexterous grasping and manipulation.
| Author | : Branden Robert Romero |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : |
| ISBN | : |
Download Soft, Round, High Resolution Tactile Fingertip Sensors for Dexterous Robotic Manipulation Book in PDF, ePub and Kindle
In this work we introduce a non-planar soft high-resolution tactile sensor. An iteration of the GelSight sensors, it enables future GelSights to have more complicated form factors, such as a humanoid fingertip. To do this we introduce a novel method for achieving directional lighting along the entirety of a curved sensor using light piping. Light piping uses total internal reflection and a semi-specular membrane to constrain the path of the light inside the sensor until the sensing membrane is deformed. By using this new membrane and changing the geometry, we introduce a new bidirectional reflectance distribution function and new optics. This require new calibration procedures in the form of developing a fisheye projection model, and developing a neighborhood and location based continuous look-up table to map the relationship between RGB value and surface normal orientation of the membrane at a point. Finally we perform two dexterous manipulation task with feedback from the sensors in the form of controlled rolling of an object on a support surface, and lid removal off a jar. We also give instructions on how to manufacture the sensor as well as increasing the durability of the membrane for all GelSight sensors.
| Author | : Tao Wang |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023 |
| Genre | : |
| ISBN | : |
Download A Novel Three-finger Dexterous Hand with Visual-based Grasp Planning and Tactile-based Stable Grasping Book in PDF, ePub and Kindle
Robotic manipulation is a complex field that still faces numerous challenges. With the development of collaborative robots, the end-effector area is just beginning to gain traction, and electrically driven end-effectors are just starting to be widely used in the industry. One of the fundamental requirements for a robot to achieve practical applications is stable grasping. However, in reality, most stable grasping relies on specific static conditions and human experience. As the robotic industry continues to develop more complex and diverse applications, the need for stable grasping to support these high-level applications increases. To achieve more complex and stable grasping functions, a complex end-effector, such as a dexterous hand, becomes essential hardware. This thesis focuses on building a novel practical dexterous hand that can be used in robotic manipulation research. To make better use of the dexterous hand, visual-based grasp planning, and tactile-based stable grasping are necessary to form a stable grasping system. In this thesis, three crucial topics were selected and divided into three parts of the work: dexterous hand, grasp planning, and stable grasping. The dexterous hand part includes the main design work of a modular three-finger dexterous hand called DoraHand and partial work on another one called Eagle Shoal. The performance of the DoraHand and the tactile sensor module is showcased through experiments. Two-finger and five-finger versions of the DoraHand have also been developed and tested in real applications, providing a reliable hardware foundation for further research. The grasp planning part focuses on providing a grasp planning solution for the dexterous hand. As an essential function of using an end-effector, this part starts with an analytic solution that considers the limitations of the dexterous hand mechanism and grasp quality evaluation. A grasp planning network has been developed using both analytic and data-driven approaches. The network features a multi-finger grasp plan representation method and has been successfully verified. The stable grasping part is the final application of this thesis, where the hardware provides the foundation and the stable grasping algorithm utilizes the tactile sensor. An open-source visual-tactile dataset has been developed using the Eagle Shoal dexterous hand. The stable grasping algorithm, built based on this dataset, has been successfully verified with different types of end-effectors, including DoraHand and suction cup gripper. Overall, these three parts of work constitute the critical components of a stable grasping system using a dexterous hand. This system and related dataset enable further research in stable grasping and robotic manipulation. The primary objective of this thesis has been successfully achieved with the development of DoraHand, which has been used by over twenty research institutes and companies. The algorithms developed for grasp planning and stable grasping serve as a foundation for future research in this field, while the dataset can be used as a benchmark for comprehensive robotic research. Further development is needed to explore the potential applications of the dexterous hand in robotic manipulation.
