Redundant robots have the potential to perform internal joints motion without modifying the pose of the end-effector by exploiting the null-space of the Jacobian matrix. Capitalizing on that feature, we developed a control technique for minimizing the robot visual appearance when observed from an eye-to-hand camera. Such algorithm is instrumental in contexts where quickly adjusting the perspective to see objects obstructed by the robot is impractical (e.g., teleoperation in narrow environment). Diminished reality techniques are frequently employed in these cases to mitigate the robot intrusion into the environment, although these techniques may sometimes compromise the perceived realism. The experimental evaluation confirmed the effectiveness of our control algorithm, demonstrating an average reduction of 4.67% of the area covered by the robot within the frame when compared to the case without the optimization action.

Multi-robot systems have gained increasing interest across various fields such as medicine, environmental monitoring, and more. Despite the evident advantages, the coordination of the swarm arises significant challenges for human operators, particularly concerning the cognitive burden needed for efficiently controlling the robots. In this study, we present a novel approach for enabling a human operator to effectively control the motion of multiple robots. Leveraging a shared control data-driven approach, we enable a single user to control the 9 degrees of freedom related to the pose and shape of a swarm. Our methodology was evaluated through an experimental campaign conducted in simulated 3D environments featuring a narrow cylindrical path, which could represent, e.g., blood vessels, industrial pipes. Subjective measures of cognitive load were assessed using a post-experiment questionnaire, comparing different levels of autonomy of the system. Results show substantial reductions in operator cognitive load when compared to conventional teleoperation techniques, accompanied by enhancements in task performance, including reduced completion times and fewer instances of contact with obstacles. This research underscores the efficacy of our approach in enhancing human-robot interaction and improving operational efficiency in multi-robot systems.

Supernumerary robotic limbs (SRLs) gained increasing interest in the last years for their applicability as healthcare and assistive technologies. These devices can either support or augment human sensorimotor capabilities, allowing users to complete tasks that are more complex than those feasible for their natural limbs. However, for a successful coordination between natural and artificial limbs, intuitiveness of interaction and perception of autonomy are key enabling features, especially for people suffering from motor disorders and impairments. The development of suitable human-robot interfaces is thus fundamental to foster the adoption of SRLs. With this work, we describe how to control an extra degree of freedom by taking advantage of what we defined the Intrinsic Kinematic Null Space, i.e. the redundancy of the human kinematic chain involved in the ongoing task. Obtained results demonstrated that the proposed control strategy is effective for performing complex tasks with a supernumerary robotic finger, and that practice improves users' control ability.

Metaverse is an immersive shared space that remote users can access through virtual and augmented reality interfaces, enabling their avatars to interact with each other and the surrounding. Although digital objects can be manipulated, physical objects cannot be touched, grasped, or moved within the metaverse due to the lack of a suitable interface. This work proposes a solution to overcome this limitation by introducing the concept of a Physical Metaverse enabled by a new interface named “Avatarm”. The Avatarm consists in an avatar enhanced with a robotic arm that performs physical manipulation tasks while remaining entirely hidden in the metaverse. The users have the illusion that the avatar is directly manipulating objects without the mediation by a robot. The Avatarm is the first step towards a new metaverse, the “Physical Metaverse,” where users can physically interact each other and with the environment.

Robotic devices for rehabilitation and training is a promising and challenging research topic with a potentially huge social impact. The availability of tools for autonomously performing physiotherapy exercises increases their efficiency, provides supplementary information about results and progress, reduces physiotherapists’ efforts and the need of their physical presence during exercise sessions, and encourages autonomy and independence in people with disabilities. Nevertheless, supportive technologies developed without the inputs and feedback of the end-user throughout the design process are less likely to be adopted for their intended purpose and use case. In this article, we propose a modular hand/wrist exoskeleton that actuates the wrist flexion/extension and adduction/abduction motions and hand fingers flexion/extension motions. It is designed to be wearable and easy to control and manage and can be used by the patient in collaboration with the physiotherapist or autonomously. A user-centered design perspective has been employed in all the design and development phases. This article introduces the main features of the device and presents some tests conducted with a user having limited hand and wrist mobility.

Avoiding face-touches has been one of the most common medical recommendations since the beginning of the COVID-19 pandemic. This work aims at providing people with help in contrasting this widespread, yet noxious habit. The solution we present exploits wearable devices to detect hand motions ending up into a face-touch and promptly notify the user exploiting haptic feedback. To this aim, we propose a recurrent neural network taking as input temporal sequences of accelerometer data acquired by a smartwatch worn by the user. The trained RNN (NFT_RNN) achieves good generalization capabilities to data coming from different users, besides a lower false detections rate with respect to a rule-based detection algorithm. The suggested solution is ready-to-use and large-scale deployable, being portable on smartwatches, fitness bands and DIY devices.

Robot rehabilitation is an emerging and promising topic that incorporates robotics with neuroscience and rehabilitation to define new methods for supporting patients with neurological diseases. As a consequence, the rehabilitation process could increase the efficacy exploiting the potentialities of robot-mediated therapies. Nevertheless, nowadays clinical effectiveness is not enough to widely introduce robotic technologies in such social contexts. In this paper we propose a step further, presenting an innovative exoskeleton for wrist flexion/extension and adduction/abduction motion training. It is designed to be wearable and easy to control and manage. It can be used by the patient in collaboration with the therapist or autonomously. The paper introduces the main steps of device design and development and presents some tests conducted with an user with limited wrist mobility.

We present the design of a highly wearable exoskeleton of hand fingers that can be used for rehabilitation applications. One of the main challenges in the design of this type of device is to reduce as much as possible the encumbrance and weight, and at the same time to guarantee performance suitable to provide a realistic and reliable motion to the user. In the proposed solution each finger is actuated by means of a single motor, and finger joint rotations are coupled to produce a natural and intuitive movement of the finger, for this reason, we exploited the concept of postural synergies. The paper presents the main design steps, the criteria adopted to choose the mechanical structure of the exoskeleton, and the features of its prototype. Compared to other solutions present in the literature, the device presented in this paper has limited weight (40 g per finger) with an interesting level of performance in terms of force (15 N).

Haptic systems have only recently started to be designed with wearability in mind. Compact, unobtrusive, inexpensive, easy-to-wear, and lightweight haptic devices enable researchers to provide compelling touch sensations to multiple parts of the body, significantly increasing the applicability of haptics in many fields, such as robotics, rehabilitation, gaming, and immersive systems. In this respect, wearable haptics has a great potential in the fields of virtual and augmented reality. Being able to touch virtual objects in a wearable and unobtrusive way may indeed open new exciting avenues for the fields of haptics and VR. This work presents a novel wearable haptic system for immersive virtual reality experiences. It conveys the sensation of touching objects made of different materials, rendering pressure and texture stimuli through a moving platform and a vibrotactile abbrv-doi-hyperref-narrowmotor. The device is composed of two platforms: one placed on the nail side of the finger and one in contact with the finger pad, connected by three cables. One small servomotor controls the length of the cables, moving the platform towards or away from the fingertip. One voice coil actuator, embedded in the platform, provides vibrotactile stimuli to the user.

We present Hand in Air Tapping (HAT), a wearable input interface which allows interactions through fingers tapping. It consists in a Bluetooth Low Energy rings enabling wireless communication with any compatible device. Each ring is hardware-wise independent of the others. This allows full modularity, i.e., the number of employed devices can be chosen to meet each application requirements. The proposed system was evaluated in two user studies, both on text input: (1) users learning curve in terms of writing speed; (2) rate of text entry comparison between the proposed interface and that of numpad style keyboards. We associated each keystroke to a set of letters/symbols and compared two approaches: one based on T9 technique and the other on multi-tap input method. Results show comparable performance between HAT and numpad style keyboards. HAT keeps the hands free, not affecting hand movements and human interactions with the surroundings. Moreover, as a general input technology, it might have several potential applications in the field of computer-human interfaces.

The human hand represents a complex fascinating system with highly sensitive sensory capabilities and dexterous grasping and manipulation functionalities. As a consequence, estimating the hand pose and at the same time having the capability to provide haptic feedback in a wearable way may benefit areas such as rehabilitation, human-robot interaction, gaming, and many more. Existing solutions allow us to accurately measure the hand configuration and provide effective force feedback to the user. However, they have limited wearability/portability. In this paper, we present the wearable sensing/actuation system glove for enhanced sensing and touching (GESTO). It is based on inertial and magnetic sensors for hand tracking, coupled with cutaneous devices for the force feedback rendering. Unlike vision-based tracking systems, the sensing glove does not suffer from occlusion problems and lighting conditions. We properly designed the cutaneous devices in order to reduce possible interferences with the magnetic sensors and performed an experimental validation on ten healthy subjects. In order to measure the estimation accuracy of GESTO, we used a high-precision optical tracker. A comparison between using the glove with and without the haptic devices shows that the presence of them does not induce a statistically significant increase in the estimation error. Experimental results revealed the effectiveness of the proposed approach. The accuracy of our system, 3.32° mean estimation error in the worst case, is comparable with the human ability of discriminating finger joint angle.

In the last years, wearable haptic technologies became very promising since they provide the users with tactile force feedback via small and wearable interfaces. However, they have no position sensing thus additional technologies like motion capture systems or expensive gloves are required. Recently, low cost hand tracking systems based on RGB-D cameras have been developed, however they suffer from lighting conditions and occlusions. In this paper we present a sensing glove based on inertial and magnetic sensors for hand tracking which can be combined with cutaneous devices for the rendering of the force feedback, thus producing a wearable sensing/actuation system. The sensing glove does not suffer from occlusion problems, it is wearable and cost effective however, since the employed sensors use the magnetometer to compensate the drift, they are sensitive to variations in the magnetic field. This property makes it challenging to interface the sensing glove with wearable devices since their motors generate variations in the magnetic field. Preliminary experiments showed the effectiveness of using inertial and magnetic sensors for hand tracking. A comparison between using the glove with and without the haptic devices was presented in order to compare the tracking performance when cutaneous devices are used.