Abstract--Diagnostic medical ultrasound is widely used, safe, and relatively low cost but requires a high degree of expertise to acquire and interpret the images. Personnel with this expertise are often not available outside of larger cities, leading to difficult, costly travel and long wait times for rural populations. T o address this issue, tele-ultrasound techniques are being developed, including robotic teleoperation and recently human teleoperation, in which a novice user is remotely guided in a hand-overhand manner through mixed reality to perform an ultrasound exam. These methods have not been compared, and their relative strengths are unknown. Human teleoperation may be more practical than robotics for small communities due to its lower cost and complexity, but this is only relevant if the performance is comparable. This paper therefore evaluates the differences between human and robotic teleoperation, examining practical aspects such as setup time and flexibility and experimentally comparing performance metrics such as completion time, position tracking, and force consistency. It is found that human teleoperation does not lead to statistically significant differences in completion time or position accuracy, with mean differences of 1.8% and 0.5%, respectively, and provides more consistent force application despite being substantially more practical and accessible. Remote and under-resourced communities have far worse access to healthcare than larger cities [1], [2]. Ultrasound has become one of the most prevalent diagnostic imaging modalities due to its relatively low cost, non-invasive nature, and lack of radiation [3], but many communities have very limited access to qualified sonographers.

Telemanipulation of deformable objects requires high precision and dexterity from the users, which can be increased by kinesthetic and tactile feedback. However, the object shape can change dynamically, causing ambiguous perception of its alignment and hence errors in the robot positioning. Therefore, the tilt angle and position classification problem has to be solved to present a clear tactile pattern to the user. This work presents a telemanipulation system for plastic pipettes consisting of a multi-contact haptic device LinkGlide to deliver haptic feedback at the users' palm and two tactile sensors array embedded in the 2-finger Robotiq gripper. We propose a novel approach based on Convolutional Neural Networks (CNN) to detect the tilt and position while grasping deformable objects. The CNN generates a mask based on recognized tilt and position data to render further multi-contact tactile stimuli provided to the user during the telemanipulation. The study has shown that using the CNN algorithm and the preset mask, tilt, and position recognition by users is increased from 9.67% using the direct data to 82.5%.

--The applications of fingertip haptic devices have spread to various fields from revolutionizing virtual reality and medical training simulations to facilitating remote robotic operations, proposing great potential for enhancing user experiences, improving training outcomes, and new forms of interaction. In this work, we present FiDT ouch, a 3D wearable haptic device that delivers cutaneous stimuli to the finger pad, such as contact, pressure, encounter, skin stretch, and vibrotactile feedback. The application of a tiny inverted Delta robot in the mechanism design allows providing accurate contact and fast changing dynamic stimuli to the finger pad surface. The performance of the developed display was evaluated in a two-stage user study of the perception of static spatial contact stimuli and skin stretch stimuli generated on the finger pad. The proposed display, by providing users with precise touch and force stimuli, can enhance user immersion and efficiency in the fields of human-computer and human-robot interactions. Fingertip haptic devices (FHD) enrich the user experience in the realm of human-computer and human-robot interaction, bridging the gap between the digital and physical worlds by providing various cutaneous and force feedback directly to the user's fingertips. The ability to accurately reproduce the feeling of grasping in a virtual or remote environment is essential for creating a realistic experience in Virtual Reality, teleoperation, and telexistence, since finger pads are used for interactions with physical objects and probing the environment in most cases.

We present a customizable soft haptic system that integrates modular hardware with an information-theoretic algorithm to personalize feedback for different users and tasks. Our platform features modular, multi-degree-of-freedom pneumatic displays, where different signal types, such as pressure, frequency, and contact area, can be activated or combined using fluidic logic circuits. These circuits simplify control by reducing reliance on specialized electronics and enabling coordinated actuation of multiple haptic elements through a compact set of inputs. Our approach allows rapid reconfiguration of haptic signal rendering through hardware-level logic switching without rewriting code. Personalization of the haptic interface is achieved through the combination of modular hardware and software-driven signal selection. To determine which display configurations will be most effective, we model haptic communication as a signal transmission problem, where an agent must convey latent information to the user. We formulate the optimization problem to identify the haptic hardware setup that maximizes the information transfer between the intended message and the user's interpretation, accounting for individual differences in sensitivity, preferences, and perceptual salience. We evaluate this framework through user studies where participants interact with reconfigurable displays under different signal combinations. Our findings support the role of modularity and personalization in creating multimodal haptic interfaces and advance the development of reconfigurable systems that adapt with users in dynamic human-machine interaction contexts.

--Robotic surgery imposes a significant cognitive burden on the surgeon. This cognitive burden increases in the case of remote robotic surgeries due to latency between entities and thus might affect the quality of surgery. Here, the patient side and the surgeon side are geographically separated by hundreds to thousands of kilometres. Real-time teleoperation of robots requires strict latency bounds for control and feedback. We propose a dual digital twin (DT) framework and explain the simulation environment and teleoperation framework. Here, the doctor visually controls the locally available DT of the patient side and thus experiences minimum latency. The second digital twin serves two purposes. Firstly, it provides a layer of safety for operator-related mishaps, and secondly, it conveys the coordinates of known and unknown objects back to the operator's side digital twin. We show that teleoperation accuracy and user experience are enhanced with our approach. Experimental results using the NASA-TLX metric show that the quality of surgery is vastly improved with DT, perhaps due to reduced cognitive burden. The network data rate for identifying objects at the operator side is 25x lower than normal.

Low-cost teleguidance of medical procedures is becoming essential to provide healthcare to remote and underserved communities. Human teleoperation is a promising new method for guiding a novice person with relatively high precision and efficiency through a mixed reality (MR) interface. Prior work has shown that the novice, or "follower", can reliably track the MR input with performance not unlike a telerobotic system. As a consequence, it is of interest to understand and control the follower's dynamics to optimize the system performance and permit stable and transparent bilateral teleoperation. To this end, linearity, time-invariance, inter-axis coupling, and passivity are important in teleoperation and controller design. This paper therefore explores these effects with regard to the follower person in human teleoperation. It is demonstrated through modeling and experiments that the follower can indeed be treated as approximately linear and time invariant, with little coupling and a large excess of passivity at practical frequencies. Furthermore, a stochastic model of the follower dynamics is derived. These results will permit controller design and analysis to improve the performance of human teleoperation.

--We introduce CoinFT, a capacitive 6-axis force / torque (F / T) sensor that is compact, light, low-cost, and robust with an average mean-squared error of 0.11 N for force and 0.84 mNm for moment when the input ranges from 0 10 N and 0 4 N in normal and shear directions, respectively. CoinFT is a stack of two rigid PCBs with comb-shaped electrodes connected by an array of silicone rubber pillars. The microcontroller interrogates the electrodes in different subsets in order to enhance sensitivity for measuring 6-axis F / T . The combination of desirable features of CoinFT enables various contact-rich robot interactions at a scale, across different embodiment domains including drones, robot end-effectors, and wearable haptic devices. We demonstrate the utility of CoinFT on drones by performing an attitude-based force control to perform tasks that require careful contact force modulation. RECISE force and torque measurement is vital for robots to perform contact-rich tasks safely and effectively. Tasks such as table wiping [1], assembly [2], or palpating soft tissue [3] require the application of force and torque within a specific range--sufficient to complete the task but not so excessive as to cause damage or waste energy. Depending on the application and interaction type, robots performing contact-rich tasks come in various forms, including robotic arms [4], grippers [5], drones [6], and wearable devices [7]. Therefore, equipping these diverse robotic platforms with sensors that can accurately measure force and torque is essential. Extensive research has been dedicated to developing 6-axis force / torque (F / T) sensors using various transduction methods [8]. Commercially available sensors also exist, such as the Gamma (A TI Industries), and 6-axis F / T sensors from MinebeaMitsumi or ReSense.

Consequently, haptics for telemanipulation is poised to become essential in the coming years, as it offers operators an additional sensory channel crucial for interpretation in extreme conditions. However, current haptic device setups are either difficult to access or provide low-quality force feedback rendering. This work proposes the design of a single-axis, open-source setup for telemanipulation development, aimed at addressing these issues. We first introduce the haptic device and demonstrate its integration with common robotic tools. The proposed joystick has the potential to accelerate the development and deployment of haptic technology in a wide range of robotics applications, enhancing operator feedback and control.

Algorithmic causal discovery is based on formal reasoning and provably converges toward the optimal solution. However, since some of the underlying assumptions are often not met in practice no applications for autonomous everyday life competence are yet available. Humans on the other hand possess full everyday competence and develop cognitive models in a data efficient manner with the ability to transfer knowledge between and to new situations. Here we investigate the causal discovery capabilities of humans in an object place task in virtual reality (VR) with haptic feedback and compare the results to the state of the art causal discovery algorithms FGES, PC and FCI. In addition we use the algorithms to analyze causal relations between sensory information and the kinematic parameters of human behavior. Our findings show that the majority of participants were able to determine which variables are causally related. This is in line with causal discovery algorithms like PC, which recover causal dependencies in the first step. However, unlike such algorithms which can identify causes and effects in our test configuration, humans are unsure in determining a causal direction. Regarding the relation between the sensory information provided to the participants and their placing actions (i.e. their kinematic parameters) the data yields a surprising dissociation of the subjects knowledge and the sensorimotor level. Knowledge of the cause-effect pairs, though undirected, should suffice to improve subject's movements. Yet a detailed causal analysis provides little evidence for any such influence. This, together with the reports of the participants, implies that instead of exploiting their consciously perceived information they leave it to the sensorimotor level to control the movement.

Tele-ultrasound has the potential greatly to improve health equity for countless remote communities. However, practical scenarios involve potentially large time delays which cause current implementations of telerobotic ultrasound (US) to fail. Using a local model of the remote environment to provide haptics to the expert operator can decrease teleoperation instability, but the delayed visual feedback remains problematic. This paper introduces a robotic tele-US system in which the local model is not only haptic, but also visual, by re-slicing and rendering a pre-acquired US sweep in real time to provide the operator a preview of what the delayed image will resemble. A prototype system is presented and tested with 15 volunteer operators. It is found that visual-haptic model-mediated teleoperation (MMT) compensates completely for time delays up to 1000 ms round trip in terms of operator effort and completion time while conventional MMT does not. Visual-haptic MMT also significantly outperforms MMT for longer time delays in terms of motion accuracy and force control. This proof-of-concept study suggests that visual-haptic MMT may facilitate remote robotic tele-US.