Individuals with visual impairments frequently depend on assistive navigation tools to live independent lives. Sensory substitution devices (SSDs) can provide navigational assistance through advancements in human sensory processing, hardware, and algorithms. In this article, we incorporate psychological mechanisms into SSD design to facilitate intuitive and measurable cross-modal translation for assisted navigation in unknown spaces. We developed LightWave, a device that translates visual distance into frequency (via touch or sound) using a cross-modal mapping function derived from psychophysical research, which demonstrates a natural correlation between visual distance and frequency across auditory and tactile modalities. Blindfolded participants navigated cluttered environments using either a traditional cane, auditory frequency cues, or tactile frequency cues. Key performance metrics, including walking distance, time, and collisions, were measured to compare effectiveness across devices. The results show that both auditory and tactile frequency navigation outperformed the traditional cane, achieving near-100% success rates compared to the cane’s 85% . Participants using the audio and tactile devices covered an average of 13 m between start and end points, compared to 18.05 m with the cane, and completed navigation faster, taking 54.8 s with audio and 50.95 s with tactile, versus 74.09 s with the cane. These findings show the potential for improving SSDs by incorporating cross-modal psychological principles, demonstrating in LightWave a novel approach to enhance personal navigation through natural intuitive feedback mechanisms.

Emerging wearable robotics demand design approaches that address not only function but also social meaning. In response, we present Sumbrella, a soft robotic garment developed as a speculative fashion probe. We first detail the design and fabrication of the Sumbrella, including sequenced origami-inspired bistable units, fabric pneumatic actuation chambers, cable-driven shape morphing mechanisms, computer vision components and an integrated wearable system comprising a hat and bolero jacket housing power and control electronics. Through a focus group with 12 creative technologists, we then used Sumbrella as a technological probe to explore how people interpreted, interacted and imagined future relationships with soft robotic wearables. While Sumbrella allowed our participants to engage in rich discussion around speculative futures and expressive potential, it also surfaced concerns about exploitation, surveillance and the personal risks and societal ethics of embedding biosensing technologies in public life. We contribute to the Human–Robot Interaction (HRI) field key considerations and recommendations for designing soft robotic garments, including the potential for kinesic communication, the impact of such technologies on social dynamics and the importance of ethical guidelines. Finally, we provide a reflection on our application of speculative design, proposing that it allows HRI researchers to not only consider functionality but also how wearable robots influence definitions of what is considered acceptable or desirable in public settings.

The inherent elasticity of soft materials can be used to create robotic grippers that deform and comply to a variety of irregular shapes. To date, several soft adaptive grasping strategies have been reported, however, most of them focus on adapting to the overall shape of the structure, while the adaptive grasping of small surface asperities is overlooked. In this paper, we propose a novel method to achieve adaptive grasping on surface asperities with a smart shape-memory silicone sponge. Heating above 60°C makes the sponge soft and deformable to allow it to penetrate within surface asperities via a pressure normal to the surface. Cooling down below 60°C makes the sponge "jam" to retain its deformed shape. The interlocking force between the jammed sponge and the asperities, and the increased area of contact, allows for adaptive grasping on asperities down to 0.4 mm with an adhesive force of up to 27.7 N in a 40 × 40 mm contacting area. We introduce the design, working principle, fabrication, and optimization of a robotic gripper based on this shape-memory silicone sponge. This sponge-jamming gripper shows great potential for developing next-generation robotic grippers for the manipulation of textured and discontinuous surfaces.

Feeling another person’s breathing is an intimate encounter that can promote deep connection, communicate affective information, and influence our physiological state. Emerging technologies are exploring the mediation of biosignals, such as breathing, between people to yield meaningful interactions. However, little is known about people’s subjective and physiological responses to mediated breath and the implications for designing these mediatory interfaces.

Soft devices employ variable stiffness to ensure safety and improve the robustness in the interaction between robots and objects. Using soft materials is one of the most popular approaches to design a variable-stiffness device, while the use of silicone sponge remains less explored in this field. Here we present a novel silicone-sponge-based variable-stiffness device (SVD). The SVD is easy-to-make and low-cost, and fabricated by an air-tight bellow enclosing a silicone sponge core. This allows easy access to the hyper-elastic response of the porous sponge whilst stiffness tuning of the device via pneumatic pressure difference. A detailed mathematical model of the SVD is proposed, by which the stiffness can be precisely controlled by the pressure difference applied. The stiffness of SVD can be tuned in the range of$[\mathbf{1.55}, \mathbf{2} \mathbf{2.82}]\times \mathbf{10}^{\mathbf{3}}\ \mathbf{N}/\mathbf{m}$, up to 14.7 times increase. The high stiffness is easily triggered by a low pressure difference$(\mathbf{\Delta} \boldsymbol{P} < \mathbf{12}\mathbf{kPa})$. The SVD is a versatile and compact module, with small axial size (10 mm height) and light weight (14.3 g), making it highly suitable for integration in a wide range of robotics and industrial applications. This, in addition to its easy-to-fabricate and low-cost features, may appeal to the robotics community at large. We further detail its working principle, fabrication processes, mathematical model and automated control methods to show its versatility.

Upper limb impairments and weakness are com-mon post-stroke and with advanced aging. Rigid exoskeletons have been developed as a potential solution, but have had limited impact. In addition to user concerns about safety, their weight and appearance, the rigid attachment and typical anchoring methods can result in skin damage. In this paper, we present a soft, fabric-based pneumatic sleeve, which can shrink from a loose fit to a tight fit in order to anchor to the limbs temporarily, thereby enabling the application of mechanical assistance only when needed. The sleeve is comfortable, ergonomic and can be embedded unobtrusively with clothing. A mathematical model is built to simulate and design sleeves with different geometric parameters. The best sleeve was capable of generating a friction force of 98 N on the limb when inflated to 25 kPa. This sleeve was used to create a wearable assistive device, integrated with a cable-driven actuator. This device was able to lift a 1.44 kg forearm rig up to 95 degree at low pressure of 20 kPa. The device was tested with six healthy participants, in terms of fit, comfort and assistive functionality. The average acceptable sleeve pressure was found to be 33±4.7 kPa. All participants liked the appearance of the sleeve, with a high average perceived assistance score of 7.33±1.6 (out of 10). The shrink-to-fit sleeve is expected to significantly increase the development and adoption of soft robotic assistive devices and emerging powered clothing.

Electroadhesion is a versatile and controllable adhesion mechanism that has been used extensively in robotics. Soft electroadhesion embodies electrostatic adhesion in soft materials and is required for shape-adaptive and safe grasping of curved objects and delicate materials. In this work, we present a soft electroadhesive fabrication method based on laser scribing graphene oxide on a silicone film, which is cost-effective, facile and green. The method can be used to generate complex electroadhesive patterns without molds or stencils. We then present a 2D finite element model to demonstrate the shape-changing behavior and electric field distributions of a dual-mode parallel dielectric elastomer actuation and coplanar electroadhesion structure. The soft electroadhesive fabrication method based on laser-scribed graphene oxide electrodes and its experimental characterization results, together with its shape-morphing simulation model are expected to enable the wider adoption of soft electroadhesion in future robotics.

Snails’ unique locomotion abilities help them realise stable movement by muscular exploiting travelling waves and friction modulation. Inspired by these characteristics, snaillike robots have recently become the focus of growing research. In this paper, we present a novel friction-driven three-foot snaillike robot which employs a simple mechanism to partially mimic and replicate snail-like motion, but in a novel form. This robot is driven by two servo motors, which makes it easy and low-cost to fabricate. The robot operates by breaking frictional symmetry in the cyclic motion of the three feet, in much the same way as the three-sphere Golestanian swimmer. The symmetry of its structure and properties of friction give the robot distinctive movements. We present a mathematical model of the robot’s locomotion, focusing on its kinetic harmonic-peristaltic movement. We designed and fabricated the robot, then undertook simulations and experiments, which closely match the analytic solutions. This robot provides a new approach to realising simpler and lower cost biomimetic mobile robots.

Actuators are essential devices that exert force and do work. The contraction of an actuator (how much it can shorten) is an important property that strongly influences its applications, especially in engineering and robotics. While high contractions have been achieved by thermally- or fluidically-driven technologies, electrically-driven actuators typically cannot contract by more than 50%. Recently developed electro-ribbon actuators are simple, low cost, scalable electroactive devices powered by dielectrophoretic liquid zipping (DLZ) that exhibit high efficiency (~70%), high power equivalent to mammalian muscle (~100 W/kg), contractions exceeding 99%. We characterise the electro-ribbon actuator and explore contraction variation with voltage and load. We describe the unique self-locking behaviour of the electro-ribbon actuator which could allow for low-power-consumption solenoids and valves. Finally, we show the interdependence of constituent material properties and the important role that material choice plays in maximising performance.

CCD camera-based tactile sensors provide high-resolution information about the deformation of soft and elastic interfaces. However, they have poor scalibility as it is difficult to sense a large surface area without increasing the distance between the camera and the interface or using multiple processing chips. For example, using such tactile sensors for a whole robotic arm is not yet possible. In this work, we demonstrate a data driven method that can reconstruct the high-resolution information about deformation of the soft interface while keeping the space requirements and power consumption relatively low. Our modified tactile sensor incorporates two independent sensing techniques, one low- and one high-resolution, and we learn to map to the latter from the former. As a low-resolution sensor, we use liquid-filled channels that transmit the information from the location of the tactile interaction to a rigid display, where the liquid displacements are tracked by a CCD camera. Simultaneously, the same interaction is measured by tracking the markers on the bottom of the sensor using a second CCD camera. After data collection, we train two different machine learning models to reconstruct the time series of the high-resolution sensor. By training a convolutional autoencoder (CAE) and attaching it to the recurrent neural network (RNN), we demonstrate the reconstruction of high-resolution video frames using only the time series of the low-resolution sensor.

Electroadhesion (EA) is an electrically controllable adhesion mechanism that has been studied and used in fields including active adhesion and attachment, robotic gripping, robotic crawling and climbing, and haptics, for over a century. This is because EA technologies, compared to other existing adhesion solutions, facilitate systems with enhanced adaptability (EA is effective on a wide of range of materials and surfaces), reduced system complexity (EA systems are both mechanically and electrically simpler), low energy consumption, and less-damaging to materials (EA, combined with soft materials, can be used to lift delicate objects). In this survey, we comprehensively detail the working principle, modeling, design, fabrication, characterization, and applications of EA technologies employed in robotics, aiming to provide guidance and offer potential insights for future EA researchers and applicants. Joint and collaborative efforts are still required to promote the in-depth understanding and mature employment of this promising adhesion and gripping technology in various robotic applications.

This paper introduces a novel form of communication via a combination of muscle sensing by electromyography and stimulation via a skin-stretcher device as a motion monitoring system. After sensing muscle activity through electromyography, the skin-stretcher device provides a skin sensation that confidentially informs or induces movements of the user who wears the device. This paper also introduces methods for translating muscle activities to the skin-stretch sensations, and additional filtering to improve the performance. In this study, we conducted preliminary experiments that demonstrate the potential of our system design.

Being aware of our body has great importance in our everyday life. It helps us to complete difficult tasks, such as movement in a dark room or grasping a complex object. These skills are important for robots as well, however, robotic bodily awareness is still an open question, and the nonlinearity of soft robots adds even more complexity. In this paper, we address this problem and present a novel method to implement bodily awareness into a real soft robot by the integration of its exteroceptive and proprioceptive sensors. We use an octopus-inspired arm as an example where the proprioceptive representation is approximated by four bend sensors integrated into the soft body, while a camera records the movement of the arm capturing its exteroceptive representation. The internal sensory signals are mapped to the visual information using a combination of a stacked convolutional autoencoder (CAE) and a recurrent neural network (RNN). As a result, the soft robot can learn to estimate and, therefore, to imagine its motion even when its visual sensor is not available.

We present a design for an energetically autonomous artificial organism, combining two subsystems; a bio-inspired energy source and bio-inspired actuation. The work is the first demonstration of energetically autonomy in a microbial fuel cell (MFC)-powered, swimming robot taking energy from it's surrounding, aqueous environment. In contrast to previous work using stacked MFC power sources, the Row-bot employs a single microbial fuel cell as an artificial stomach and uses commercially available voltage step-up hardware to produce usable voltages. The energy generated exceeds the energy requirement to complete the mechanical actuation needed to refuel. Energy production and actuation are demonstrated separately with the results showing that the combination of these subsystems will produce closed-loop energetic autonomy. The work shows a crucial step in the development of autonomous robots capable of long term self-power. Bio-inspiration for the design of the Row-bot was taken from the water boatman beetle. This proof of concept study opens many avenues for the further development of the subsystems comprising the Row-bot, and the functionality of the robot itself.

Tactile force sensing and compliance are key elements of safe and natural-feeling human-robot interaction. We present an optical tactile sensor in the form of a compliant elastomer 'fingertip' tracked by a high-speed low-resolution image sensor with on-board signal processing. We propose a dual-mode bio-mimetic control loop, where in reflex mode the sensor sends fast reflexive action commands directly to actuators, bypassing the central controller to minimise reaction times. For higher-level interpretation, a slower explore mode enables more sophisticated processing of the sensory input by the central controller. We demonstrate sensing of normal force in both modes of operation, showing that in reflex mode we are able to rapidly detect the presence of forces and compute an approximate magnitude estimate while in explore mode we are able to perform more accurate force measurements.

In this paper we compare a variety of unsupervised probabilistic models used to represent a data set consisting of textual and image information. We show that those based on latent Dirichlet allocation (LDA) out perform traditional mixture models in likelihood comparison. The data set is taken from radiology; a combination of medical images and consultants reports. The task of learning to classify individual tissue, or disease types, requires expert hand labeled data. This is both: expensive to produce and prone to inconsistencies in labeling. Here we present methods that require no hand labeling and also automatically discover sub-types of disease. The learnt models can be used for both prediction and classification of new unseen data.

We present a new method for tagging image regions using uncertain information granules. This tagging forms an efficient route for the elicitation of knowledge from domain experts with respect to images. We then use this uncertain granular information to train a fuzzy machine learner and then to classify unseen images. This method is particularly suited to applications where an expert input into the classification process is essential but where the expert's time is in extremely short supply. Results are presented within the example domain of detecting the lung disease from computed tomography scans.

Object-oriented modelling with words seeks to extend the new field of modelling with words, itself derived from computing with words, with features of object-oriented knowledge representation and programming. We show that object-oriented modelling with words has two key benefits: 1) the uncertain class hierarchy provides a natural knowledge representation framework for real-world problems; and 2) the object-oriented paradigm enforces a strict software engineering design and implementation process. In this paper we present a new approach to object-oriented modelling with words based on a new theory of inheritance where object memberships and property applicabilities are uncertain. We present the uncertain object-oriented logic programming language Fril++ as a tool for object-oriented modelling with words. We present an example application of object-oriented modelling with words: an extendible object-oriented data browser and machine learning environment.

User modelling is fast becoming a major topic for commercial and academic research. A major problem in user modelling is in categorising a new user into known user classes. We consider user classification as a recognition problem where a new user is an uncertain object of undetermined class and we must determine the membership of the object in each of a number of prototypical user classes. The temporal nature of user behaviour suggests a belief updating approach to user recognition. We examine the FILUM approach to user recognition and present a more generalised model. We compare the generalised FILUM model to a new interval support version of Hogarth and Einhorn's (1992) anchor and adjustment belief updating approach. We present an example using the iterated prisoner's dilemma problem implemented in the Fril++ object oriented uncertain logic programming language.

Logic programming, object-oriented programming and soft computing have provided advantageous methodologies and techniques for computer-based problem solving. This paper proposes a framework that combines these three disciplines to exploit their own advantages in dealing with real world problems. The framework is a logic-based one in which class and object properties are represented by clauses. Vague data in properties are represented by fuzzy sets interpreted as possibility distributions. Uncertain applicability of a property to a class or an object is represented either by a support pair defining probability lower and upper bounds, or by a certainty lower bound. Fundamental issues of uncertain membership and inheritance are then discussed and solutions to them are proposed. The result forms a basis for development of soft computing object-oriented programming systems.