The Rice Haptic Rocker: Skin stretch haptic feedback with the Pisa/IIT SoftHand

Touch provides an important cue to perceive the physical properties of the external objects. Recent studies showed that tactile sensation also contributes to our sense of hand position and displacement in perceptual tasks. In this study, we tested the hypothesis that, sliding our hand over a stationary surface, tactile motion may provide a feedback for guiding hand trajectory. We asked participants to touch a plate having parallel ridges at different orientations and to perform a self-paced, straight movement of the hand. In our daily-life experience, tactile slip motion is equal and opposite to hand motion. Here, we used a well-established perceptual illusion to dissociate, in a controlled manner, the two motionestimates. According to previous studies, this stimulus produces a bias in the perceived direction of tactile motion, predicted by tactile flow model. We showed a systematic deviation in the movement of the hand towards a direction opposite to the one predicted by tactile flow, supporting the hypothesis that touch contributes to motor control of the hand. We suggested a model where the perceived hand motion is equal to a weighted sum of the estimate from classical proprioceptive cues (e.g., from musculoskeletal system) and the estimate from tactile slip

Pouring by Feel: An Analysis of Tactile and Proprioceptive Sensing for Accurate Pouring

As service robots begin to be deployed to assist humans, it is important for them to be able to perform a skill as ubiquitous as pouring. Specifically, we focus on the task of pouring an exact amount of water without any environmental instrumentation, that is, using only the robot's own sensors to perform this task in a general way robustly. In our approach we use a simple PID controller which uses the measured change in weight of the held container to supervise the pour. Unlike previous methods which use specialized force-torque sensors at the robot wrist, we use our robot joint torque sensors and investigate the added benefit of tactile sensors at the fingertips. We train three estimators from data which regress the poured weight out of the source container and show that we can accurately pour within 10 ml of the target on average while being robust enough to pour at novel locations and with different grasps on the source container

A Perspective on Cephalopods Mimicry and Bioinspired Technologies toward Proprioceptive Autonomous Soft Robots

Octopus skin is an amazing source of inspiration for bioinspired sensors, actuators and control solutions in soft robotics. Soft organic materials, biomacromolecules and protein ingredients in octopus skin combined with a distributed intelligence, result in adaptive displays that can control emerging optical behavior, and 3D surface textures with rough geometries, with a remarkably high control speed (≈ms). To be able to replicate deformable and compliant materials capable of translating mechanical perturbations in molecular and structural chromogenic outputs, could be a glorious achievement in materials science and in the technological field. Soft robots are suitable platforms for soft multi-responsive materials, which can provide them with improved mechanical proprioception and related smarter behaviors. Indeed, a system provided with a “learning and recognition” functions, and a constitutive “mechanical” and “material intelligence” can result in an improved morphological adaptation in multi-variate environments responding to external and internal stimuli. This review aims to explore challenges and opportunities related to smart and chromogenic responsive materials for adaptive displays, reconfigurable and programmable soft skin, proprioceptive sensing system, and synthetic nervous control units for data processing, toward autonomous soft robots able to communicate and interact with users in open-world scenarios

Soft Morphological Computation

Soft Robotics is a relatively new area of research, where progress in material science has powered the next generation of robots, exhibiting biological-like properties such as soft/elastic tissues, compliance, resilience and more besides. One of the issues when employing soft robotics technologies is the soft nature of the interactions arising between the robot and its environment. These interactions are complex, and the their dynamics are non-linear and hard to capture with known models. In this thesis we argue that complex soft interactions can actually be beneficial to the robot, and give rise to rich stimuli which can be used for the resolution of robot tasks. We further argue that the usefulness of these interactions depends on statistical regularities, or structure, that appear in the stimuli. To this end, robots should appropriately employ their morphology and their actions, to influence the system-environment interactions such that structure can arise in the stimuli. In this thesis we show that learning processes can be used to perform such a task. Following this rationale, this thesis proposes and supports the theory of Soft Morphological Computation (SoMComp), by which a soft robot should appropriately condition, or ‘affect’, the soft interactions to improve the quality of the physical stimuli arising from it. SoMComp is composed of four main principles, i.e.: Soft Proprioception, Soft Sensing, Soft Morphology and Soft Actuation. Each of these principles is explored in the context of haptic object recognition or object handling in soft robots. Finally, this thesis provides an overview of this research and its future directions.AHDB CP17

Design Considerations for 3D Printed, Soft, Multimaterial Resistive Sensors for Soft Robotics

Sensor design for soft robots is a challenging problem because of the wide range of design parameters (e.g., geometry, material, actuation type, etc.) critical to their function. While conventional rigid sensors work effectively for soft robotics in specific situations, sensors that are directly integrated into the bodies of soft robots could help improve both their exteroceptive and interoceptive capabilities. To address this challenge, we designed sensors that can be co-fabricated with soft robot bodies using commercial 3D printers, without additional modification. We describe an approach to the design and fabrication of compliant, resistive soft sensors using a Connex3 Objet350 multimaterial printer and investigated an analytical comparison to sensors of similar geometries. The sensors consist of layers of commercial photopolymers with varying conductivities. We characterized the conductivity of TangoPlus, TangoBlackPlus, VeroClear, and Support705 materials under various conditions and demonstrate applications in which we can take advantage of these embedded sensors

Model-free Soft-Structure Reconstruction for Proprioception using Tactile Arrays

Continuum body structures provide unique opportunities for soft robotics, with the infinite degrees of freedom enabling unconstrained and highly adaptive exploration and manipulation. However, the infinite degrees of freedom of continuum bodies makes sensing (both intrinsically and extrinsically) challenging. To address this, in this paper we propose a model-free method for sensorizing tentacle-like continuum soft-structures using an array of spatially arranged capacitive tactile sensors. By using visual tracking, the relationship between the tactile response and the 3D shape of the continuum soft-structure can be learned. A data set of 15000 random soft-body postures was used, with recorded camera-tracked positions logged synchronously to the tactile sensor responses. This was used to train a neural network which can predict posture. We show it is possible to achieve proprioceptive awareness over all three axis of motion in space, reconstructing the body structure and inferring the soft body head’s pose with an average accuracy of ≈ 1mm in comparison to the visual tracked counterpart. To demonstrate the capabilities of the system, we perform random exploration of environments limiting the work-space of the sensorized robot. We find the method capable to autonomously reconstruct the reachable morphology of the environment without the need of external sensing units.This work was funded by the UK Agriculture and Horticulture Development Board (CP 172) and Physical Sciences Research Council (EPSRC) MOTION grant [EP/N03211X/2

On the development of a cybernetic prosthetic hand

The human hand is the end organ of the upper limb, which in humans serves the important function of prehension, as well as being an important organ for sensation and communication. It is a marvellous example of how a complex mechanism can be implemented, capable of realizing very complex and useful tasks using a very effective combination of mechanisms, sensing, actuation and control functions. In this thesis, the road towards the realization of a cybernetic hand has been presented. After a detailed analysis of the model, the human hand, a deep review of the state of the art of artificial hands has been carried out. In particular, the performance of prosthetic hands used in clinical practice has been compared with the research prototypes, both for prosthetic and for robotic applications. By following a biomechatronic approach, i.e. by comparing the characteristics of these hands with the natural model, the human hand, the limitations of current artificial devices will be put in evidence, thus outlining the design goals for a new cybernetic device. Three hand prototypes with a high number of degrees of freedom have been realized and tested: the first one uses microactuators embedded inside the structure of the fingers, and the second and third prototypes exploit the concept of microactuation in order to increase the dexterity of the hand while maintaining the simplicity for the control. In particular, a framework for the definition and realization of the closed-loop electromyographic control of these devices has been presented and implemented. The results were quite promising, putting in evidence that, in the future, there could be two different approaches for the realization of artificial devices. On one side there could be the EMG-controlled hands, with compliant fingers but only one active degree of freedom. On the other side, more performing artificial hands could be directly interfaced with the peripheral nervous system, thus establishing a bi-directional communication with the human brain

Mesure tactile proprioceptive pour des doigts sous-actionnés

RÉSUMÉ La préhension et la manipulation d’objets par des robots deviennent de plus en plus répandues dans divers domaines, et ce, pour de multiples applications. L’utilisation de robots permet d’améliorer la répétabilité, la rapidité et la précision lors de certaines tâches, et ce, comparativement aux performances d’un opérateur humain. De plus, un robot peut également être conçu pour accomplir certaines tâches qu’une personne ne pourrait effectuer, que ce soit au niveau de la force nécessaire ou du manque d’espace pour manoeuvrer. Des robots peuvent également plus aisément fonctionner dans des environnements hostiles. Tout comme pour l’être humain, la rétroaction tactile est particulièrement utile et même inévitable pour effectuer certaines tâches. Il faut toutefois souligner qu’il s’agit d’un thème de recherche où l’on est encore bien loin d’avoir atteint les performances humaines. Pour s’en approcher, de nombreuses et diverses technologies de capteurs tactiles existent, mais chacune comporte ses défauts. Ainsi, bien qu’il existe actuellement des solutions technologiques pour donner une rétroaction sensorielle à un robot ou à son opérateur, ces dernières s’avèrent généralement coûteuses, présentent différents défauts au niveau de la sensibilité et ne sont pas toujours adaptées à certaines utilisations. Dans l’optique de trouver une alternative efficace aux technologies conventionnelles de détection et de mesure tactiles, la présente thèse se concentre sur la possibilité d’utiliser la raideur inhérente du mécanisme de transmission d’un doigt sous-actionné. En effet, les doigts et les mains sous-actionnés sont de plus en plus communément utilisés pour leur simplicité propre et leur capacité à saisir et à s’adapter à la forme d’objet de manière purement mécanique sans schéma de commande complexe ou de nombreux actionneurs. Contrairement aux mécanismes pleinement actionnés, les doigts sous-actionnés, communément appelés adaptatifs, comportent des éléments passifs pour contraindre leur mouvement avant le contact, tout en permettant d’obtenir une prise stable sans développer des forces de contact trop élevées initialement. Les doigts sous-actionnés étant généralement dépourvus d’actionneurs à l’intérieur du doigt lui-même, les seuls capteurs déjà présents sont typiquement situés à l’unique actionneur. Toutefois, en analysant et traitant en temps réel les données de ces capteurs internes, également appelés proprioceptifs, il est possible d’extraire une panoplie d’informations sur ce qui se passe au niveau des phalanges. Ce principe est donc utilisé pour obtenir des algorithmes de détection tactile pouvant être utilisés sur différents systèmes, tels qu’une pince compliante et un préhenseurs à membrures.----------ABSTRACT Robotic hands have become more and more prevalent in many fields. They have replaced human operators in many repetitive applications where robots become more precise and efficient. Moreover, robotic graspers can lift heavier loads and accomplish maneuvers a human could not. They can also manipulate objects in hostile environments where it would be dangerous for humans. Therefore, a lot of work has been done in recent years to improve their capabilities such as their speed, dexterity, strength, and versatility. However, current robotic manipulators lack the sensory feedback of their human counterparts. Indeed, haptic and tactile feedbacks are still very limited in current devices, which may be a problem, because tactile sensing is deemed nearly mandatory for a large number of applications. Conventional tactile sensors, which are usually applied on the external surface of a robot, are generally used, but they can also be costly, insensitive to some dynamic phenomena, and not adequate to some applications. To solve these issues, many authors have worked on finding alternatives to standard tactile sensors. This thesis fits in this current trend by focusing on the possibility of using the internal stiffness of underactuated fingers to design a virtual tactile sensor. This technique is referred to as proprioceptive tactile sensing. It is applied here to underactuated robotics fingers, which are becoming prevalent in many fields. Underactuated mechanisms, sometimes referred to as self-adaptive, are particularly interesting because of their intrinsic ability to mechanically adapt themselves to the shape of an object without complex control laws and as low as only one actuator. As they have by definition less actuators, they generally have no sensor in the finger’s mechanism itself. Instead of adding new sensors, it is possible to take advantage of the sensors already present, such as the ones at the actuator. Therefore, in this thesis, only data provided by sensors at the actuator is used. Since a oneto-one relationship exists between the contact location and the instantaneous stiffness of the mechanism, it is possible to compute one from the other. Therefore, with the measurements from sensors at the actuator, it is possible to estimate the point of contact. To this aim, a complete model is proposed and experimental data is provided. Different algorithms were tested successfully on a compliant biocompatible gripper and a 2-DOF linkage-driven finger. Finally, an optimization procedure is presented with the aim of finding the optimal parameters of the transmission mechanism to improve the sensitivity of the virtual tactile sensor. The data presented in this thesis demonstrate the robustness of the proposed proprioceptive tactile sensing (PTS) technique

Dual-Modality Haptic Feedback Improves Dexterous Task Execution with Virtual EMG-Controlled Gripper

Upper-extremity amputees who use myoelectric prostheses currently lack the haptic sensory information needed to perform dexterous activities of daily living. While considerable research has focused on restoring this haptic information, these approaches often rely on single-modality feedback schemes which are necessary but insufficient for the feedforward and feedback control strategies employed by the central nervous system. Multi-modality feedback approaches have been gaining attention in several application domains, however, the utility for myoelectric prosthesis use remains unclear. In this study, we investigated the utility of dual-modality haptic feedback in a virtual EMG-controlled grasp-and-hold task with a brittle object and variable load force. We recruited N=20 non-amputee participants to perform the task in four conditions: no feedback, vibration feedback of incipient slip, squeezing feedback of grip force, and dual (vibration + squeezing) feedback of incipient slip and grip force. Results suggest that receiving any feedback is better than receiving none, however, dual-modality feedback is far superior to either single-modality feedback approach in terms of preventing the object from breaking or dropping, even after it started slipping. Control with dual-modality feedback was also seen as more intuitive than with either of the single-modality feedback approaches