Tactile sensor | EPFL Graph Search

A tactile sensor is a device that measures information arising from physical interaction with its environment. Tactile sensors are generally modeled after the biological sense of cutaneous touch which is capable of detecting stimuli resulting from mechanical stimulation, temperature, and pain (although pain sensing is not common in artificial tactile sensors). Tactile sensors are used in robotics, computer hardware and security systems. A common application of tactile sensors is in touchscreen devices on mobile phones and computing. Tactile sensors may be of different types including piezoresistive, piezoelectric, optical, capacitive and elastoresistive sensors. Uses Tactile sensors appear in everyday life such as elevator buttons and lamps which dim or brighten by touching the base. There are also innumerable other applications for tactile sensors of which most people are never aware. Sensors that measure very small changes must have very high sensitivities. Sensors n

Flight of mind: Sensorimotor and multisensory embodiment with aviation robotics, flight simulator, and virtual reality

Myeong Seop Song

Normally, humans experience an 'I' as residing in one's body and as the agent of one's actions. In other words, the self is experienced as being inside the body (self-location), as having a body (self-identification), and as being able to control the person's body (agency). Over the last decade, research in cognitive neuroscience has revealed that vestibular signals contribute to such aspects of the embodied self. On the other hand, the influences of embodiment on vestibular sensations have received scant attention. In my thesis, I highlight how changes in multisensory and sensorimotor embodiment impact vestibular sensations. For this purpose, I merged technologies from photorealistic virtual reality (VR), aviation robotics such as a drone, flight simulator, haptics with knowledge of cognitive neuroscience about embodiment and bodily self-consciousness (BSC). The multidisciplinary approaches provide new paradigms to modulate embodiment and investigate related vestibular sensations based on multisensory and sensorimotor mechanisms of BSC. Also, the approaches reveal the relationship between embodiment and vestibular sensations. In Study 1, we have objectively shown the possibility of the duplication of the self by measuring peripersonal space (PPS). To this aim, we provided participants with real-time feedback from a drone-mounted camera that autonomously followed them while they responded to multisensory stimuli on their body and in their environment and walked forward. Our results demonstrate that this setup induces two spatially distinct PPSs in healthy participants: reaction times for visuotactile stimuli were faster around both the physical body and the observed body. In Studies 2 & 3, we have added a photorealistic avatar and a haptic vest to a robotic flight simulator to enable and investigate personalized immersive flight experiences in healthy humans. Study 2 showed that modulations of embodiment using sensorimotor stimulation resulted in enhanced subjective flying experience, better piloting performance, and learning. In Study 3, we showed that multisensory - visual and tactile - stimulation could further increase embodiment and improve piloting performance. Furthermore, in both studies, we found a positive relationship between embodiment and flight sensations. In Studies 4 & 5, we developed a new multisensory body scanning technology platform to induce and investigate one of the most common experiences in which the center of awareness is dissociated from the physical body: out-of-body experiences (OBEs). OBEs represent an interesting link between embodiment and vestibular sensations as they are characterized by embodiment of an elevated aerial position and perspective in space that is commonly associated with prominent sensations of floating, elevation, and flight. In this study, we have investigated how several sensorimotor and audio-visual stimulation conditions affect subjective sensations of disembodiment, floating, lightness as well as behavioral measures of self-location. In conclusion, my work developed new paradigms to investigate embodiment, BSC, and vestibular processing. My work revealed the influence of embodiment on vestibular sensations and applied these insights between embodiment and vestibular sensations to the field of field engineering by combining state-of-the-art technologies with knowledge of BSC with broad implications for robotics, neuroscience, of immersive VR, as well as experiential

EPFL2020

Dynamic Grasp Adaptation

Miao Li

The human hand is an amazing tool, demonstrated by its incredible motor capability and remarkable sense of touch. To enable robots to work in a human-centric environment, it is desirable to endow robotic hands with human-like capabilities for grasping and object manipulation. However, due to its inherent complexity and inevitable model uncertainty, robotic grasping and manipulation remains a challenge. This thesis focuses on grasp adaptation in the face of model and sensing uncertainties: Given an object whose properties are not known with certainty (e.g., shape, weight and external perturbation), and a multifingered robotic hand, we aim at determining where to put the fingers and how the fingers should adaptively interact with the object using tactile sensing, in order to achieve either a stable grasp or a desired dynamic behaviour. A central idea in this thesis is the object-centric dynamics: namely, that we express all control constraints into an object-centric representation. This simplifies computa- tion and makes the control versatile to the type of hands. This is an essential feature that distinguishes our work from other robust grasping work in the literature, where generating a static stable grasp for a given hand is usually the primary goal. In this thesis, grasp adaptation is a dynamic process that flexibly adapts the grasp to fit some purpose from the objectâs perspective, in the presence of a variety of uncertainties and/or perturbations. When building a grasp adaptation for a given situation, there are two key problems that must be addressed: 1) the problem of choosing an initial grasp that is suitable for future adaptation, and more importantly 2) the problem of design- ing an adaptation strategy that can react adequately to achieve desired behaviour of the grasped object. To address challenge 1 (planning a grasp under shape uncertainty), we propose an approach to parameterizing the uncertainty in object shape using Gaussian Processes (GPs) and incorporate it as a constraint into contact-level grasp planning. To realize the planned contacts using different hands interchangeably, we further develop a prob- abilistic model to predict the feasible hand configurations, including hand pose and finger joints, given the desired contact points only. The model is built using the con- cept of Virtual Frame(VF), and it is independent from the choice of hand frame and object frame. The performance of the proposed approach is validated on two differ- ent robotic hands, an industrial gripper (4 DOF Barrett hand) and a humanoid hand (16 DOF Allegro hand) to manipulate objects of daily use with complex geometry and various texture (a spray bottle, a tea caddy, a jug and a bunny toy). In the second part of this thesis, we propose an approach to the design of adapta- tion strategy to ensure grasp stability in the presence of physical uncertainties of objects(object weight, friction at contacts and external perturbation). Based on an object-level impedance controller, we first design a grasp stability estimator in the object frame using the grasp experience and tactile sensing. Once a grasp is predicted to be unstable during online execution, the grasp adaptation strategy is triggered to improve the grasp stability, by either changing the stiffness at finger level or relocating the position of one fingertip to a better area.

EPFL2016

Data fusion for a hand prosthesis tactile feedback system

Christian Enz, Huaiqi Huang, Jörn Justiz, Tao Li, Ozan Unsal

Current myoelectrically controlled hand prostheses normally lack tactile feedback, thus limiting their functionalities and user acceptance. We propose a non-invasive tactile sensory feedback system, consisting miniaturized sensors, wireless communication module and vibrotactile actuators, aiming at providing a natural sense of touch to amputees. We model the tactile feedback chain from tactile sensors to actuators. This model includes a 2D hand model, a sensor data fusion model, and a phantom map model. We implemented the sensor data fusion model by different techniques and compared their performance.

IEEE2014