A Novel Skin-Stretch Haptic Device for Intuitive Control of Robotic Prostheses and Avatars

Without proprioception, i.e., the intrinsic capability of a body to perceive its own limb position, completing daily life activities would require constant visual attention and it would be challenging or even impossible. This situation is similar to the one experienced after limb amputation and in robotic tele-operation, where the natural sensory-motor loop is broken. While some promising solutions based on skin stretch sensory substitution have been proposed to restore tactile properties in these conditions, there is still room for enhancing the intuitiveness of stimulus delivery and integration of haptic feedback devices within user's body. To contribute to this goal, here, we propose a wearable device based on skin stretch stimulation, the Stretch-Pro, which can provide proprioceptive information on artificial hand aperture. This system can be suitably integrated in a prosthetic socket or can be easily worn by a user controlling remote robots. The system can imitate the stretching of the skin that would naturally occur on the intact limb, when it is used to accomplish motor tasks. Two versions of the system are presented, with one and two actuators, respectively, which deliver the stretch stimulus in different ways. Experiments with able-bodied participants and a preliminary test with one prosthesis user are reported. Results suggest that Stretch-Pro could be a viable solution to convey proprioceptive cues to upper limb prosthesis users, opening promising perspectives for tele-robotics applications

Intuitive Robot Teleoperation through Multi-Sensor Informed Mixed Reality Visual Aids

© 2021 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.Mobile robotic systems have evolved to include sensors capable of truthfully describing robot status and operating environment as accurately and reliably as never before. This possibility is challenged by effective sensor data exploitation, because of the cognitive load an operator is exposed to, due to the large amount of data and time-dependency constraints. This paper addresses this challenge in remote-vehicle teleoperation by proposing an intuitive way to present sensor data to users by means of using mixed reality and visual aids within the user interface. We propose a method for organizing information presentation and a set of visual aids to facilitate visual communication of data in teleoperation control panels. The resulting sensor-information presentation appears coherent and intuitive, making it easier for an operator to catch and comprehend information meaning. This increases situational awareness and speeds up decision-making. Our method is implemented on a real mobile robotic system operating outdoor equipped with on-board internal and external sensors, GPS, and a reconstructed 3D graphical model provided by an assistant drone. Experimentation verified feasibility while intuitive and comprehensive visual communication was confirmed through a qualitative assessment, which encourages further developments.Peer reviewe

On Robots and Insurance

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LHF Connect: a DIY telepresence robot against COVID-19

This contribution describes a case study of a “do-it-yourself” (DIY) opensource service and related product to help combating the COVID-19 emergency. It illustrates the birth of LHF Connect, a project designed to facilitate communication between patients isolated in COVID-19 hospitals’ ward and their relatives. LHF Connect is a teleoperated robot that can move in autonomy around the hospital. A User Centered Design approach, methods and specific tools helped in managing crucial steps of the design process such as i) the collection of needs coming from the context, stakeholders and end-users; ii) defining the service blueprint; iii) imagining finishing concepts; and iv) managing the communication activities. The initiative has been promoted by a multidisciplinary team of researchers (mainly roboticists with the help of specific competences coming from Design discipline)

State of the Art About Remote Laboratories Paradigms - Foundations of Ongoing Mutations

9 pages. Litterature review made fall 2007 on exisiting Remote Laboratories approaches and technologies.International audienceIn this paper, we provide a literature review of modern remote laboratories. According to this state-of-theart, we explain why remote laboratories are at a technological crossroad, whereas they were slugging for a decade. From various observations based on our review, we try to identify possible evolutions for the next generation of remote laboratories

DeepDynamicHand: A Deep Neural Architecture for Labeling Hand Manipulation Strategies in Video Sources Exploiting Temporal Information

Humans are capable of complex manipulation interactions with the environment, relying on the intrinsic adaptability and compliance of their hands. Recently, soft robotic manipulation has attempted to reproduce such an extraordinary behavior, through the design of deformable yet robust end-effectors. To this goal, the investigation of human behavior has become crucial to correctly inform technological developments of robotic hands that can successfully exploit environmental constraint as humans actually do. Among the different tools robotics can leverage on to achieve this objective, deep learning has emerged as a promising approach for the study and then the implementation of neuro-scientific observations on the artificial side. However, current approaches tend to neglect the dynamic nature of hand pose recognition problems, limiting the effectiveness of these techniques in identifying sequences of manipulation primitives underpinning action generation, e.g., during purposeful interaction with the environment. In this work, we propose a vision-based supervised Hand Pose Recognition method which, for the first time, takes into account temporal information to identify meaningful sequences of actions in grasping and manipulation tasks. More specifically, we apply Deep Neural Networks to automatically learn features from hand posture images that consist of frames extracted from grasping and manipulation task videos with objects and external environmental constraints. For training purposes, videos are divided into intervals, each associated to a specific action by a human supervisor. The proposed algorithm combines a Convolutional Neural Network to detect the hand within each video frame and a Recurrent Neural Network to predict the hand action in the current frame, while taking into consideration the history of actions performed in the previous frames. Experimental validation has been performed on two datasets of dynamic hand-centric strategies, where subjects regularly interact with objects and environment. Proposed architecture achieved a very good classification accuracy on both datasets, reaching performance up to 94%, and outperforming state of the art techniques. The outcomes of this study can be successfully applied to robotics, e.g., for planning and control of soft anthropomorphic manipulators

Neuromorphic vibrotactile stimulation of fingertips for encoding object stiffness in telepresence sensory substitution and augmentation applications

We present a tactile telepresence system for real-time transmission of information about object stiffness to the human fingertips. Experimental tests were performed across two laboratories (Italy and Ireland). In the Italian laboratory, a mechatronic sensing platform indented different rubber samples. Information about rubber stiffness was converted into on-off events using a neuronal spiking model and sent to a vibrotactile glove in the Irish laboratory. Participants discriminated the variation of the stiffness of stimuli according to a two-alternative forced choice protocol. Stiffness discrimination was based on the variation of the temporal pattern of spikes generated during the indentation of the rubber samples. The results suggest that vibrotactile stimulation can effectively simulate surface stiffness when using neuronal spiking models to trigger vibrations in the haptic interface. Specifically, fractional variations of stiffness down to 0.67 were significantly discriminated with the developed neuromorphic haptic interface. This is a performance comparable, though slightly worse, to the threshold obtained in a benchmark experiment evaluating the same set of stimuli naturally with the own hand. Our paper presents a bioinspired method for delivering sensory feedback about object properties to human skin based on contingency-mimetic neuronal models, and can be useful for the design of high performance haptic devices

The Shape of Damping: Optimizing Damping Coefficients to Improve Transparency on Bilateral Telemanipulation

This thesis presents a novel optimization-based passivity control algorithm for hapticenabled bilateral teleoperation systems involving multiple degrees of freedom. In particular, in the context of energy-bounding control, the contribution focuses on the implementation of a passivity layer for an existing time-domain scheme, ensuring optimal transparency of the interaction along subsets of the environment space which are preponderant for the given task, while preserving the energy bounds required for passivity. The involved optimization problem is convex and amenable to real-time implementation. The effectiveness of the proposed design is validated via an experiment performed on a virtual teleoperated environment. The interplay between transparency and stability is a critical aspect in haptic-enabled bilateral teleoperation control. While it is important to present the user with the true impedance of the environment, destabilizing factors such as time delays, stiff environments, and a relaxed grasp on the master device may compromise the stability and safety of the system. Passivity has been exploited as one of the the main tools for providing sufficient conditions for stable teleoperation in several controller design approaches, such as the scattering algorithm, timedomain passivity control, energy bounding algorithm, and passive set position modulation. In this work it is presented an innovative energy-based approach, which builds upon existing time-domain passivity controllers, improving and extending their effectiveness and functionality. The set of damping coefficients are prioritized in each degree of freedom, the resulting transparency presents a realistic force feedback in comparison to the other directions. Thus, the prioritization takes effect using a quadratic programming algorithm to find the optimal values for the damping. Finally, the energy tanks approach on passivity control is a solution used to ensure stability in a system for robotics bilateral manipulation. The bilateral telemanipulation must maintain the principle of passivity in all moments to preserve the system\u2019s stability. This work presents a brief introduction to haptic devices as a master component on the telemanipulation chain; the end effector in the slave side is a representation of an interactive object within an environment having a force sensor as feedback signal. The whole interface is designed into a cross-platform framework named ROS, where the user interacts with the system. Experimental results are presented