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| Author | : Moore School of Electrical Engineering. Department of Computer and Information Science |
| Publisher | : |
| Total Pages | : |
| Release | : 1983 |
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| Author | : Huaping Liu |
| Publisher | : Springer |
| Total Pages | : 220 |
| Release | : 2018-03-20 |
| Genre | : Computers |
| ISBN | : 9811061718 |
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 | : Qiang Li |
| Publisher | : Academic Press |
| Total Pages | : 374 |
| Release | : 2022-04-02 |
| Genre | : Computers |
| ISBN | : 0323904173 |
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 | : Kenneth Gutierrez |
| Publisher | : |
| Total Pages | : 119 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
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 | : Ravinder S. Dahiya |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 258 |
| Release | : 2012-07-29 |
| Genre | : Technology & Engineering |
| ISBN | : 9400705794 |
Future robots are expected to work closely and interact safely with real-world objects and humans alike. Sense of touch is important in this context, as it helps estimate properties such as shape, texture, hardness, material type and many more; provides action related information, such as slip detection; and helps carrying out actions such as rolling an object between fingers without dropping it. This book presents an in-depth description of the solutions available for gathering tactile data, obtaining aforementioned tactile information from the data and effectively using the same in various robotic tasks. The efforts during last four decades or so have yielded a wide spectrum of tactile sensing technologies and engineered solutions for both intrinsic and extrinsic touch sensors. Nowadays, new materials and structures are being explored for obtaining robotic skin with physical features like bendable, conformable, and stretchable. Such features are important for covering various body parts of robots or 3D surfaces. Nonetheless, there exist many more hardware, software and application related issues that must be considered to make tactile sensing an effective component of future robotic platforms. This book presents an in-depth analysis of various system related issues and presents the trade-offs one may face while developing an effective tactile sensing system. For this purpose, human touch sensing has also been explored. The design hints coming out of the investigations into human sense of touch can be useful in improving the effectiveness of tactile sensory modality in robotics and other machines. Better integration of tactile sensors on a robot’s body is prerequisite for the effective utilization of tactile data. The concept of semiconductor devices based sensors is an interesting one, as it allows compact and fast tactile sensing systems with capabilities such as human-like spatio-temporal resolution. This book presents a comprehensive description of semiconductor devices based tactile sensing. In particular, novel Piezo Oxide Semiconductor Field Effect Transistor (POSFET) based approach for high resolution tactile sensing has been discussed in detail. Finally, the extension of semiconductors devices based sensors concept to large and flexile areas has been discussed for obtaining robotic or electronic skin. With its multidisciplinary scope, this book is suitable for graduate students and researchers coming from diverse areas such robotics (bio-robots, humanoids, rehabilitation etc.), applied materials, humans touch sensing, electronics, microsystems, and instrumentation. To better explain the concepts the text is supported by large number of figures.
| Author | : Maria Bauza Villalonga |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : |
| ISBN | : |
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 | : Yasemin Vardar |
| Publisher | : Springer Nature |
| Total Pages | : 148 |
| Release | : 2020-11-09 |
| Genre | : Computers |
| ISBN | : 3030522520 |
This book explains the mechanisms underpinning the tactile perception of electrovibration and lays the groundwork for delivering realistic haptic feedback on touchscreens via this method. Effective utilization of electrovibration can only be accomplished by simultaneously investigating both the physical and perceptual aspects of the finger-touchscreen interaction. Towards this goal, present work blends the available knowledge on electromechanical properties of the human finger and human tactile perception with the results of new psychophysical experiments and physical measurements. By following such an approach that combines both theoretical and experimental information, the study proposes new methods and insights on generating realistic haptic effects, such as textures and edges on these displays. Besides, state-of-the-art research on the field is reviewed, and future work is discussed. The presented interdisciplinary methods and insights can interest students, broad communities of haptics, neuroscience, engineering, physics, and cognitive sciences, as well as user-interaction experts and product designers from the industry.
| Author | : Matteo Fumagalli |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 115 |
| Release | : 2013-08-31 |
| Genre | : Technology & Engineering |
| ISBN | : 3319011227 |
This thesis proposes an effective methodology for enhancing the perceptual capabilities and achieving interaction control of the iCub humanoid robot. The method is based on the integration of measurements from different sensors (force/torque, inertial and tactile sensors) distributed along the robot’s kinematic chain. Humanoid robots require a substantial amount of sensor information to create their own representations of the surrounding environment. Tactile perception is of primary importance for the exploration process. Also in humans, the tactile system is completely functional at birth. In humanoid robotics, the measurements of forces and torques that the robot exchanges with its surroundings are essential for safe interaction with the environment and with humans. The approach proposed in this thesis can successfully enhance the perceptual capabilities of robots by exploiting only a limited number of both localized and distributed sensors, providing a feasible and convenient solution for achieving active compliance control of humanoid robots.
| Author | : Anna V. Petrovskaya |
| Publisher | : Stanford University |
| Total Pages | : 226 |
| Release | : 2011 |
| Genre | : |
| ISBN | : |
Reliable perception is required in order for robots to operate safely in unpredictable and complex human environments. However, reliability of perceptual inference algorithms has been poorly studied so far. These algorithms capture uncertain knowledge about the world in the form of probabilistic belief distributions. A number of Monte Carlo and deterministic approaches have been developed, but their efficiency depends on the degree of smoothness of the beliefs. In the real world, the smoothness assumption often fails, leading to unreliable perceptual inference results. Motivated by concrete robotics problems, we propose two novel perceptual inference algorithms that explicitly consider local non-smoothness of beliefs and adapt to it. Both of these algorithms fall into the category of iterative divide-and-conquer methods and hence scale logarithmically with desired accuracy. The first algorithm is termed Scaling Series. It is an iterative Monte Carlo technique coupled with annealing. Local non-smoothness is accounted for by sampling strategy and by annealing schedule. The second algorithm is termed GRAB, which stands for Guaranteed Recursive Adaptive Bounding. GRAB is an iterative adaptive grid algorithm, which relies on bounds. In this case, local non-smoothness is captured in terms of local bounds and grid resolution. Scaling Series works well for beliefs with sharp transitions, but without many discontinuities. GRAB is most appropriate for beliefs with many discontinuities. Both of these algorithms far outperform the prior art in terms of reliability, efficiency, and accuracy. GRAB is also able to guarantee that a quality approximation of the belief is produced. The proposed algorithms are evaluated on a diverse set of real robotics problems: tactile perception, autonomous driving, and mobile manipulation. In tactile perception, we localize objects in 3D starting with very high initial uncertainty and estimating all 6 degrees of freedom. The localization is performed based on tactile sensory data. Using Scaling Series, we obtain highly accurate and reliable results in under 1 second. Improved tactile object localization contributes to manufacturing applications, where tactile perception is widely used for workpiece localization. It also enables robotic applications in situations where vision can be obstructed, such as rescue robotics and underwater robotics. In autonomous driving, we detect and track vehicles in the vicinity of the robot based on 2D and 3D laser range finders. In addition to estimating position and velocity of vehicles, we also model and estimate their geometric shape. The geometric model leads to highly accurate estimates of pose and velocity for each vehicle. It also greatly simplifies association of data, which are often split up into separate clusters due to occlusion. The proposed Scaling Series algorithm greatly improves reliability and ensures that the problem is solved within tight real time constraints of autonomous driving. In mobile manipulation, we achieve highly accurate robot localization based on commonly used 2D laser range finders using the GRAB algorithm. We show that the high accuracy allows robots to navigate in tight spaces and manipulate objects without having to sense them directly. We demonstrate our approach on the example of simultaneous building navigation, door handle manipulation, and door opening. We also propose hybrid environment models, which combine high resolution polygons for objects of interest with low resolution occupancy grid representations for the rest of the environment. High accuracy indoor localization contributes directly to home/office mobile robotics as well as to future robotics applications in construction, inspection, and maintenance of buildings. Based on the success of the proposed perceptual inference algorithms in the concrete robotics problems, it is our hope that this thesis will serve as a starting point for further development of highly reliable perceptual inference methods.
| Author | : Lin, Xi |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
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.
