Antonio Frisoli

Andrea Mattia Garavagno, Daniele Leonardis, Antonio Frisoli

The current trend of applying transfer learning from convolutional neural networks (CNNs) trained on large datasets can be an overkill when the target application is a custom and delimited problem, with enough data to train a network from scratch. On the other hand, the training of custom and lighter CNNs requires expertise, in the from-scratch case, and or high-end resources, as in the case of hardware-aware neural architecture search (HW NAS), limiting access to the technology by non-habitual NN developers. For this reason, we present ColabNAS, an affordable HW NAS technique for producing lightweight task-specific CNNs. Its novel derivative-free search strategy, inspired by Occam's razor, allows to obtain state-of-the-art results on the Visual Wake Word dataset, a standard TinyML benchmark, in just 3.1 GPU hours using free online GPU services such as Google Colaboratory and Kaggle Kernel.

Rui Chen, Daniele Leonardis, Antonio Frisoli

Wearable fingertip haptic devices are critical for realistic interaction in virtual reality, augmented reality, and teleoperation, yet existing approaches struggle to simultaneously achieve adequate tactile output, low mass, simple fabrication, and untethered portability. Here we show that fabric-based pneumatic actuation can address this gap. Our device comprises four pneumatic chambers fabricated from thermoplastic polyurethane-coated fabric via computer numerical control heat-sealing, yielding a soft, conformable interface weighing 2.1 g that operates untethered with a wrist-mounted control unit. Mechanical and dynamic characterization confirms that the fabric actuators produce sufficient force, displacement, and bandwidth for fingertip tactile rendering. A psychophysical study with 15 participants demonstrates classification accuracy exceeding 90% across three distinct tactile modes -- contact configuration, directional sliding, and vibrotactile frequency. These findings establish fabric-based pneumatic actuation as a viable technology route for lightweight, low-cost, and multimodal fingertip haptic interfaces.

Camilla Celli, Andrea Bini, Valerio Novelli et al.

In haptics, guaranteeing stability is essential to ensure safe interaction with remote or virtual environments. One of the most relevant methods at the state-of-the-art is the Time Domain Passivity Approach (TDPA). However, its high conservatism leads to a significant degradation of transparency. Moreover, the stabilizing action may conflict with the device's physical limitations. State-of-the-art solutions have attempted to address these actuator limits, but they still fail to account simultaneously for the power limits of each actuator while maximizing transparency. This work proposes a new damping limitation method based on prioritized dissipation actions. It prioritizes an optimal dissipation direction that minimizes actuator load, while any excess dissipation is allocated to the orthogonal hyperplane. The solution provides a closed-form formulation and is robust in multi-DoF scenarios, even in the presence of actuator and motion anisotropies. The method is experimentally validated using a parallel haptic interface interacting with a virtual environment and tested under different operating conditions.

Rui Chen, Firman Isma Serdana, Domenico Chiaradia et al.

Hand impairment following neurological disorders substantially limits independence in activities of daily living, motivating the development of effective assistive and rehabilitation strategies. Soft robotic gloves have attracted growing interest in this context, yet persistent challenges in customization, ergonomic fit, and flexion-extension actuation constrain their clinical utility. Here, we present a dual-action fabric-based soft robotic glove incorporating customized actuators aligned with individual finger joints. The glove comprises five independently controlled dual-action actuators supporting finger flexion and extension, together with a dedicated thumb abduction actuator. Leveraging computer numerical control heat sealing technology, we fabricated symmetrical-chamber actuators that adopt a concave outer surface upon inflation, thereby maximizing finger contact area and improving comfort. Systematic characterization confirmed that the actuators generate sufficient joint moment and fingertip force for ADL-relevant tasks, and that the complete glove system produces adequate grasping force for common household objects. A preliminary study with ten healthy subjects demonstrated that active glove assistance significantly reduces forearm muscle activity during object manipulation. A pilot feasibility study with three individuals with cervical spinal cord injury across seven functional tasks indicated that glove assistance promotes more natural grasp patterns and reduces reliance on tenodesis grasp, although at the cost of increased task completion time attributable to the current actuation interface. This customizable, ergonomic design represents a practical step toward personalized hand rehabilitation and assistive robotics.

Rui Chen, Xianlong Mai, Alireza Sanaei et al.

Object manipulation is fundamental to virtual reality (VR) applications, yet conventional fingertip haptic devices fail to render certain tactile features relevant for immersive and precise interactions, as i.e. detection of edges. This paper presents a compact, lightweight fingertip haptic device (24.3 g) that delivers distinguishable surface and edge contact feedback through a novel dual-motor mechanism. Pressure distribution characterization using a 6 x 6 flexible sensor array demonstrates distinct contact patterns between the two stimulation modes. A preliminary user study with five participants achieved 93% average classification accuracy across four conditions (edge/surface contact with light/heavy pressure), with mean response times of 2.79 seconds. The results indicate that the proposed device can effectively convey edge and surface tactile cues, potentially enhancing object manipulation fidelity in VR environments.

Andrea Mattia Garavagno, Edoardo Ragusa, Antonio Frisoli et al.

This paper presents an automatic method for the design of Neural Networks (NNs) at the edge, enabling Machine Learning (ML) access even in privacy-sensitive Internet of Things (IoT) applications. The proposed method runs on IoT gateways and designs NNs for connected sensor nodes without sharing the collected data outside the local network, keeping the data in the site of collection. This approach has the potential to enable ML for Healthcare Internet of Things (HIoT) and Industrial Internet of Things (IIoT), designing hardware-friendly and custom NNs at the edge for personalized healthcare and advanced industrial services such as quality control, predictive maintenance, or fault diagnosis. By preventing data from being disclosed to cloud services, this method safeguards sensitive information, including industrial secrets and personal data. The outcomes of a thorough experimental session confirm that -- on the Visual Wake Words dataset -- the proposed approach can achieve state-of-the-art results by exploiting a search procedure that runs in less than 10 hours on the Raspberry Pi Zero 2.

Mine Sarac, Massimiliano Solazzi, Edoardo Sotgiu et al.

This study presents the design and the kinematic optimization of a novel, underactuated, linkage-based robotic hand exoskeleton to assist users in performing grasping tasks. The device has been designed to apply only normal forces to the finger phalanges during flexion/extension of the fingers, while providing automatic adaptability for different finger sizes. Thus, the easiness of the attachment to the user's fingers and better comfort have been ensured. The analyses of the device kinematic pose, statics, and stability of grasp have been performed. These analyses have been used to optimize the link lengths of the mechanism, ensuring that a reasonable range of motion is satisfied while maximizing the force transmission on the finger joints. Finally, the usability of a prototype with multiple fingers has been tested during grasping tasks with different objects.

Mine Sarac, Massimiliano Solazzi, Antonio Frisoli

Most current hand exoskeletons have been designed specifically for rehabilitation, assistive or haptic applications to simplify the design requirements. Clinical studies on post-stroke rehabilitation have shown that adapting assistive or haptic applications into physical therapy sessions significantly improves the motor learning and treatment process. The recent technology can lead to the creation of generic hand exoskeletons that are application-agnostic. In this paper, our motivation is to create guidelines and best practices for generic exoskeletons by reviewing the literature of current devices. First, we describe each application and briefly explain their design requirements, and then list the design selections to achieve these requirements. Then, we detail each selection by investigating the existing exoskeletons based on their design choices, and by highlighting their impact on application types. With the motivation of creating efficient generic exoskeletons in the future, we finally summarize the best practices in the literature.

Tobias Klamt, Diego Rodriguez, Lorenzo Baccelliere et al.

Mobile manipulation robots have high potential to support rescue forces in disaster-response missions. Despite the difficulties imposed by real-world scenarios, robots are promising to perform mission tasks from a safe distance. In the CENTAURO project, we developed a disaster-response system which consists of the highly flexible Centauro robot and suitable control interfaces including an immersive tele-presence suit and support-operator controls on different levels of autonomy. In this article, we give an overview of the final CENTAURO system. In particular, we explain several high-level design decisions and how those were derived from requirements and extensive experience of Kerntechnische Hilfsdienst GmbH, Karlsruhe, Germany (KHG). We focus on components which were recently integrated and report about a systematic evaluation which demonstrated system capabilities and revealed valuable insights.

Tobias Klamt, Max Schwarz, Christian Lenz et al.

Solving mobile manipulation tasks in inaccessible and dangerous environments is an important application of robots to support humans. Example domains are construction and maintenance of manned and unmanned stations on the moon and other planets. Suitable platforms require flexible and robust hardware, a locomotion approach that allows for navigating a wide variety of terrains, dexterous manipulation capabilities, and respective user interfaces. We present the CENTAURO system which has been designed for these requirements and consists of the Centauro robot and a set of advanced operator interfaces with complementary strength enabling the system to solve a wide range of realistic mobile manipulation tasks. The robot possesses a centaur-like body plan and is driven by torque-controlled compliant actuators. Four articulated legs ending in steerable wheels allow for omnidirectional driving as well as for making steps. An anthropomorphic upper body with two arms ending in five-finger hands enables human-like manipulation. The robot perceives its environment through a suite of multimodal sensors. The resulting platform complexity goes beyond the complexity of most known systems which puts the focus on a suitable operator interface. An operator can control the robot through a telepresence suit, which allows for flexibly solving a large variety of mobile manipulation tasks. Locomotion and manipulation functionalities on different levels of autonomy support the operation. The proposed user interfaces enable solving a wide variety of tasks without previous task-specific training. The integrated system is evaluated in numerous teleoperated experiments that are described along with lessons learned.

Diego Rodriguez, Antonio Di Guardo, Antonio Frisoli et al.

Every time a person encounters an object with a given degree of familiarity, he/she immediately knows how to grasp it. Adaptation of the movement of the hand according to the object geometry happens effortlessly because of the accumulated knowledge of previous experiences grasping similar objects. In this paper, we present a novel method for inferring grasp configurations based on the object shape. Grasping knowledge is gathered in a synergy space of the robotic hand built by following a human grasping taxonomy. The synergy space is constructed through human demonstrations employing a exoskeleton that provides force feedback, which provides the advantage of evaluating the quality of the grasp. The shape descriptor is obtained by means of a categorical non-rigid registration that encodes typical intra-class variations. This approach is especially suitable for on-line scenarios where only a portion of the object's surface is observable. This method is demonstrated through simulation and real robot experiments by grasping objects never seen before by the robot.