This project will investigate how daily encounters with digital technologies and the sense of rapid comprehension that they require can be used to make tactile and contemplative visual artwork. Completed over 3 years, studio work will be undertaken with the goal of making art that engages with a physical experience of time in terms of the range of technologically complex and disembodied ways of communicating today.

A comparative study of three six-axis force sensors selected after an extensive literature survey is presented. A sensor to measure ground contact force at each foot of a walking machine is recommended. Principles of force sensing are reviewed and characteristics of sensing elements are discussed. Results of simulation of three six-axis force sensors are presented as behavior curves, sensitivity plots and compliance matrices. These simulations use finite element techniques. Condition numbers of compliance matrices are presented as a measure of overall sensor performance. Estimates of manufacturing costs are included as a final selection criterion. / Graduation date: 1994

The manipulation contact forces convey substantial information about the manipulation state. This paper address the fundamental problem of interpreting the force signals without any additional manipulation context. Techniques based on forms of the generalized sequential likelihood ratio test are used to segment individual strain signals into statistically equivalent pieces. We report on our experimental development of the segmentation algorithm and on its results for contact states. The sequential likelihood ratio test is reviewed and some of its special cases and optimal properties are discussed. Finally, we conclude by discussing extensions to the techniques and a contact interpretation framework.

The goal of this research is to develop the prototype of a tactile sensing platform for anthropomorphic manipulation research. We investigate this problem through the fabrication and simple control of a planar 2-DOF robotic finger inspired by anatomic consistency, self-containment, and adaptability. The robot is equipped with a tactile sensor array based on optical transducer technology whereby localized changes in light intensity within an illuminated foam substrate correspond to the distribution and magnitude of forces applied to the sensor surface plane. The integration of tactile perception is a key component in realizing robotic systems which organically interact with the world. Such natural behavior is characterized by compliant performance that can initiate internal, and respond to external, force application in a dynamic environment. However, most of the current manipulators that support some form of haptic feedback either solely derive proprioceptive sensation or only limit tactile sensors to the mechanical fingertips. These constraints are due to the technological challenges involved in high resolution, multi-point tactile perception. In this work, however, we take the opposite approach, emphasizing the role of full-finger tactile feedback in the refinement of manual capabilities. To this end, we propose and implement a control framework for sensorimotor coordination analogous to infant-level grasping and fixturing reflexes. This thesis details the mechanisms used to achieve these sensory, actuation, and control objectives, along with the design philosophies and biological influences behind them. The results of behavioral experiments with a simple tactilely-modulated control scheme are also described. The hope is to integrate the modular finger into an %engineered analog of the human hand with a complete haptic system.

Grasping and manipulation of objects is an integral part of a robot’s physical interaction with the environment. In order to cope with real-world situations, sensor based grasping of objects and grasp stability estimation is an important skill. This thesis addresses the problem of predicting the stability of a grasp from the perceptions available to a robot once fingers close around the object before attempting to lift it. A regrasping step can be triggered if an unstable grasp is identified. The percepts considered consist of object features (visual), gripper configurations (proprioceptive) and tactile imprints (haptic) when fingers contact the object. This thesis studies tactile based stability estimation by applying machine learning methods such as Hidden Markov Models. An approach to integrate visual and tactile feedback is also introduced to further improve the predictions of grasp stability, using Kernel Logistic Regression models. Like humans, robots are expected to grasp and manipulate objects in a goal-oriented manner. In other words, objects should be grasped so to afford subsequent actions: if I am to hammer a nail, the hammer should be grasped so to afford hammering. Most of the work on grasping commonly addresses only the problem of finding a stable grasp without considering the task/action a robot is supposed to fulfill with an object. This thesis also studies grasp stability assessment in a task-oriented way based on a generative approach using probabilistic graphical models, Bayesian Networks. We integrate high-level task information introduced by a teacher in a supervised setting with low-level stability requirements acquired through a robot’s exploration. The graphical model is used to encode probabilistic relationships between tasks and sensory data (visual, tactile and proprioceptive). The generative modeling approach enables inference of appropriate grasping configurations, as well as prediction of grasp stability. Overall, results indicate that the idea of exploiting learning approaches for grasp stability assessment is applicable in realistic scenarios. / <p>QC 20121026</p>

No description available.

Texture segmentation is an effortless process in scene analysis, yet its neural mechanisms are not sufficiently understood. A common assumption in most current approaches is that texture segmentation is a vision problem. However, considering that texture is basically a surface property, this assumption can at times be misleading. One interesting possibility is that texture may be more intimately related with touch than with vision. Recent neurophysiological findings showed that receptive fields (RFs) for touch resemble that of vision, albeit with some subtle differences. To leverage on this, here I propose three ways to investigate the tactile receptive fields in the context of texture processing: (1) performance, (2) development, and (3) analysis. For performance, I tested how such distinct properties in tactile receptive fields can affect texture segmentation performance, as compared to that of visual receptive fields. Preliminary results suggest that touch has an advantage over vision in texture segmentation. These results support the idea that texture is fundamentally a tactile (surface) property. The next question is what drives the two types of RFs, visual and tactile, to become different during cortical development? I investigated the possibility that tactile RF and visual RF emerge based on the same cortical learning process, where the only difference is in the input type, natural-scene-like vs. texture-like. The main result is that RFs trained on natural scenes develop RFs resembling visual RFs, while those trained on texture resemble tactile RFs. These results again suggest a tight link between texture and the tactile modality, from a developmental context. To investigate further the functional properties of these RFs in texture processing, the response of tactile RFs and visual RFs were analyzed with manifold learning and with statistical approaches. The results showed that touch-based manifold seems more suitable for texture processing and desirable properties found in visual RF response can carry over to those in the tactile domain. These results are expected to shed new light on the role of tactile perception of texture; help develop more powerful, biologically inspired texture segmentation algorithms; and further clarify the differences and similarities between touch and vision.

Robots can move, see, and navigate in the real world outside carefully structured factories, but they cannot yet grasp and manipulate objects without human intervention. Two key barriers are the complexity of current approaches, which require complicated hardware or precise perception to function effectively, and the challenge of understanding system performance in a tractable manner given the wide range of factors that impact successful grasping. This thesis presents sensors and simple control algorithms that relax the requirements on robot hardware, and a framework to understand the capabilities and limitations of grasping systems. / Engineering and Applied Sciences

Advancements in movement restoration have accelerated in recent years while the restoration of somatosensation has progressed relatively slowly. This dissertation attempts to partially correct this oversight by developing an electrotactile feedback system that might be used to restore the sense of touch.Initially, the perceptual parameters of the skin regions likely to be used as a source of tactile information (the fingertip) and as a destination for electrotactile feedback (the back of the neck) were evaluated. The perceptual parameters of tactile threshold sensitivity, spatial acuity, and gain scaling were collected from subjects for both regions of skin. These same parameters were also gathered in response to electrotactile stimulation of the neck. The threshold sensitivity and spatial acuity of the fingertip was found to be far superior to that on the back of the neck, yet the mechanical perceptual gain scaling parameters of the neck were similar to that of the finger tip. Yet, the psychometric functions for electrical stimulation on the neck differed markedly in gain sensitivity from that of mechanical stimulation. A mapping function between the two modalities was then calculated based upon the tactile and electrotacile characterization data that was collected.An electrotactile feedback system was then developed based upon the calculated mapping function, allowing conversion of force applied to an artificial sensor on the fingertip to a perceptually equivalent electrical stimulus on the neck. The system proved to be quite effective: Subjects were able to effectively evaluate electrical stimulus that was derived from application of force to the sensor on the fingertip. The perceptual gain scaling for the feedback system matched that of natural mechanical stimulation.A grip force matching task was evaluated in test subjects under three conditions: a) normal tactile sensation, b) anesthesia of the fingers, and c) anesthesia of the fingers with restored tactile information via the electrotactile feedback system. The relative loss in grip-force matching ability when tactile feedback was abolished by local anesthetic was mild, indicating a strong ability for individuals to generate target force levels using other forms of feedback. Electrotactile feedback, therefore, offered only modest improvement when deployed in the anesthetized hand.

Thesis (M.S.)--University of Texas at El Paso, 2007. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.