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Soft robots promise solutions for a wide range of applications that cannot be achieved with traditional, rigid-component robots. A key challenge is the creation of robotic structures that can vary their stiffness at will, for example, by using antagonistic actuators, to optimize their interaction with the environment and be able to exert high forces.

Optical fiber bending, deformation or shape sensing are important measurement technologies and have been widely deployed in various applications including healthcare, structural monitoring and robotics. However, existing optical fiber bending sensors require complex sensor structures and interrogation systems. Here, inspired by the recent renewed interest in information-rich multimode optical fibers, we show that the multimode fiber (MMF) output speckles contain the three-dimensional (3D) geometric shape information of the MMF itself. We demonstrate proof-of-concept 3D multi-point deformation sensing via a single multimode fiber by using k -nearest neighbor (KNN) machine learning algorithm, and achieve a classification accuracy close to 100%. Our results show that a single MMF based deformation sensor is excellent in terms of system simplicity, resolution and sensitivity, and can be a promising candidate in deformation monitoring or shape-sensing applications.

This paper presents experimental evidence for the existence of a set of unique force modulation strategies during manual soft tissue palpation to locate hard abnormalities such as tumors. We explore the active probing strategies of defined local areas and outline the role of force control. In addition, we investigate whether the applied force depends on the non-homogeneity of the soft tissue. Experimental results on manual palpation of soft silicone phantoms show that humans have a well defined force control pattern of probing that is used independently of the non-homogeneity of the soft tissue. We observed that the modulations of lateral forces are distributed around the mean frequency of 22.3 Hz. Furthermore, we found that the applied normal pressure during probing can be modeled using a second order reactive autoregressive model. These mathematical abstractions were implemented and validated for the autonomous palpation for different stiffness parameters using a robotic probe with a rigid spherical indentation tip. The results show that the autonomous robotic palpation strategy abstracted from human demonstrations is capable of not only detecting the embedded nodules, but also enhancing the stiffness perception compared to static indentation of the probe.

Minimally Invasive Surgery is often limited by the lack of tactile feedback. Indeed, surgeons have traditionally relied heavily on tactile feedback to estimate tissue stiffness - a critical factor in both diagnostics and treatment. With this in mind we present in this paper a flexible sensor foil, based on polymer optical waveguide. This sensor has been applied for real-time contact force measurement, material stiffness differentiation and surface texture reconstruction. Interrogated by a commercially available optoelectronic device, the sensor foil offers precise and reproducible feedback of contact forces up to 5 N, with a minimal detectable limit of 0.1 N. It also demonstrates distinct optical attenuation responses when indenting silicone samples of varying stiffnesses under controlled displacement. When integrated onto a 3D-printed module resembling an endoscopic camera and manipulated by a robotic arm, the sensor successfully generated spatial stiffness mapsof a phantom. Moreover, by sliding over structures with varying surface textures, the sensor foil was able to reconstruct surface profiles based on the light attenuation responses. The results demonstrate that the presented sensor foil possesses great potential for surgical applications by providing additional haptic information to surgeons.

Tactile perception is a feature benefiting reliable grasping and manipulation. This paper presents the design of an integrated fingertip force sensor employing an optical fiber based approach where applied forces modulate light intensity. The proposed sensor system is developed to support grasping of a broad range of objects, including those that are hard as well those that are soft. The sensor system is comprised of four sensing elements forming a tactile array integrated with the tip of a finger. We investigate the design configuration of a separate force sensing element with the aim to improve its measurement range. The force measurement of a single tactile element is based on a two-level displacement that is achieved thanks to a hybrid sensing structure made up of a stiff linear and flexible ortho-planar spring. An important outcome of this paper is a miniature tactile fingertip sensor that is capable of perceiving light contact, typically occurring during the initial stages of a grasp, as well as measuring higher forces, commonly present during tight grasps.

Soft-growing robots based on the eversion principle are renowned for their ability to rapidly extend along their longitudinal axis, allowing them to access remote, confined, or otherwise inaccessible spaces. Their inherently compliant structure enables safe interaction with delicate environments, while their simple actuation mechanisms support lightweight and low-cost designs. Despite these benefits, implementing effective navigation mechanisms remains a significant challenge. Previous research has explored the use of pneumatic artificial muscles mounted externally on the robot’s body, which, when contracting, induce directional bending. However, this method only offers limited bending performance. To enhance maneuverability, pneumatic artificial muscles embedded in between the walls of double-walled eversion robots have also been considered and shown to offer superior bending performance and force output as compared to externally attached muscle. However, their adoption has been hindered by the complexity of the current manufacturing techniques, which require individually sealing the artificial muscles. To overcome this multi-stage fabrication approach in which muscles are embedded one by one, we propose a novel single-step method. The key to our approach is the use of non-heat-sealable inserts to form air channels during the sealing process. This significantly simplifies the process, reducing production time and effort and improving scalability for manufacturing, potentially enabling mass production. We evaluate the fabrication speed and bending performance of robots produced in this manner and benchmark them against those described in the literature. The results demonstrate that our technique offers high bending performance and significantly improves the manufacturing efficiency.

The false attribution of autonomy and related concepts to artificial agents that lack the attributed levels of the respective characteristic is problematic in many ways. In this article, we contrast this view with a positive viewpoint that emphasizes the potential role of such false attributions in the context of robotic language acquisition. By adding emotional displays and congruent body behaviors to a child-like humanoid robot’s behavioral repertoire, we were able to bring naïve human tutors to engage in so called intent interpretations. In developmental psychology, intent interpretations can be hypothesized to play a central role in the acquisition of emotion, volition, and similar autonomy-related words. The aforementioned experiments originally targeted the acquisition of linguistic negation. However, participants produced other affect- and motivation-related words with high frequencies too and, as a consequence, these entered the robot’s active vocabulary. We will analyze participants’ non-negative emotional and volitional speech and contrast it with participants’ speech in a non-affective baseline scenario. Implications of these findings for robotic language acquisition in particular and artificial intelligence and robotics more generally will also be discussed.

Hand rehabilitation exoskeletons are in need of improving key features such as simplicity, compactness, bi-directional actuation, low cost, portability, safe human-robotic interaction, and intuitive control. This article presents a brain-controlled hand exoskeleton based on a multi-segment mechanism driven by a steel spring. Active rehabilitation training is realized using a threshold of the attention value measured by an electroencephalography (EEG) sensor as a brain-controlled switch for the hand exoskeleton. We present a prototype implementation of this rigid-soft combined multi-segment mechanism with active training and provide a preliminary evaluation. The experimental results showed that the proposed mechanism could generate enough range of motion with a single input by distributing an actuated linear motion into the rotational motions of finger joints during finger flexion/extension. The average attention value in the experiment of concentration with visual guidance was significantly higher than that in the experiment without visual guidance. The feasibility of the attention-based control with visual guidance was proven with an overall exoskeleton actuation success rate of 95.54% (14 human subjects). In the exoskeleton actuation experiment using the general threshold, it performed just as good as using the customized thresholds; therefore, a general threshold of the attention value can be set for a certain group of users in hand exoskeleton activation.

Grasp affordances in robotics represent different ways to grasp an object involving a variety of factors from vision to hand control. A model of grasp affordances that is able to scale across different objects, features and domains is needed to provide robots with advanced manipulation skills. The existing frameworks, however, can be difficult to extend towards a more general and domain independent approach. This work is the first step towards a modular implementation of grasp affordances that can be separated into two stages: approach to grasp and grasp execution. In this study, human experiments of approaching to grasp are analysed, and object-independent patterns of motion are defined and modelled analytically from the data. Human subjects performed a specific action (hammering) using objects of different geometry, size and weight. Motion capture data relating the hand-object approach distance was used for the analysis. The results showed that approach to grasp can be structured in four distinct phases that are best represented by non-linear models, independent from the objects being handled. This suggests that approaching to grasp patterns are following an intentionally planned control strategy, rather than implementing a reactive execution.

This paper presents a multi-axis force/torque sensor based on simply-supported beam and optoelectronic technology. The sensor’s main advantages are: (1) Low power consumption; (2) low-level noise in comparison with conventional methods of force sensing (e.g., using strain gauges); (3) the ability to be embedded into different mechanical structures; (4) miniaturisation; (5) simple manufacture and customisation to fit a wide-range of robot systems; and (6) low-cost fabrication and assembly of sensor structure. For these reasons, the proposed multi-axis force/torque sensor can be used in a wide range of application areas including medical robotics, manufacturing, and areas involving human–robot interaction. This paper shows the application of our concept of a force/torque sensor to flexible continuum manipulators: A cylindrical MIS (Minimally Invasive Surgery) robot, and includes its design, fabrication, and evaluation tests.