Fast-legged humanoid robots are transforming industries from manufacturing to medical robotics, with the global market projected to grow from $0.67 billion in 2024 to $2.27 billion by 2033 at a 14.3% CAGR. Despite rapid advancements, challenges remain in navigating complex terrains, especially uneven, deformable, and high-friction surfaces. This paper presents the first minimally sensorised blade leg made by coupling soft and rigid materials for robots: an alternative approach for multimodal sensing and advanced control algorithms in terrain navigation. This incorporates a passive leg design embedded with barometric pressure sensors that are proven to retain high dimentional spatio-temporal data. Hence we hypothesized that barometric pressure sensors can capture multidimensional terrain data and subtle surface compliance changes through spatiotemporal pressure patterns. The blade was mounted on an UR5 robotic arm and tested in terrains of varied textures, including aluminium, pebble, coir, and sandpaper; materials spanning a diverse range of stiffness. Spatiotemporal data from the sensors were recorded and analyzed to assess terrain characteristics and leg-terrain interactions under different conditions. The results demonstrated that barometric pressure sensors could accurately recognize different terrains with as few as three sensors in a 2-second time frame. Recognition accuracy improved with more sensors, demonstrating the effectiveness of morphologically adapted composite structures with optimally placed minimal sensors.

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.

Many robotic and biological systems exploit their morphology’s interaction with the environment to become more adaptable, more energy efficient, and to simplify their control. The principles of morphological computation (MC) have been increasingly studied in recent years and researchers have investigated theoretical approaches to quantify the contribution of MC for a variety of robotic systems using only simulated models. In this work, we quantify MC in a physical robotic system, utilizing position-controlled legs with two degrees of freedom in two designs of different elastic compliance, on a bespoke test rig to execute a walking gait. The contribution of morphology was estimated by applying a theoretical model at various stages within the gait cycle to quantify the MC. The relationships between MC and the ground reaction forces and actuator energy consumption are analyzed. The results indicating that increasing the compliance in the leg morphology increases the mean MC value (7.70±1.49) relative to a traditional leg design (5.03±2.27). Periods of high MC were found to occur during the swing phase of the walking gait and reduced during the stance phase with ground reaction forces, which correlates with the findings of prior theoretical studies of MC in hopping gaits. The benefits of refining the leg morphology for higher MC is demonstrated by the measurements of cost of transport (COT), where the leg with the higher mean MC of 7.70 has a lower mean COT of 102.8 compared to the other leg’s mean COT of 153.8. The results demonstrate how real-world measurements of MC may help design the morphology of improved robotics systems.

In this paper we present IRIS, a manipulator that is capable of applying contact forces when interacting with an object/stimulus, in increments in the order of mN up to 6N at 5 radial locations simultaneously. IRIS is based upon the contractual mechanism of its namesake: the iris diaphragm often found in cameras. Complete coverage of the surface of a realistic breast phantom is demonstrated using this contractual mechanism combined with control over the angle of incidence between the sensors and the stimulus using sim-to-real concepts. A significant amount of the complexity in control is outsourced to the morphology and compliance of the mechanism. The manipulator demonstrates the technological feasibility of a robotic clinical breast examination.

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.

We propose a paradigm called Skin-On interfaces, in which interactive devices have their own (artificial) skin, thus enabling new forms of input gestures for end-users (e.g. twist, scratch). Our work explores the design space of Skin-On interfaces by following a bio-driven approach: (1) From a sensory point of view, we study how to reproduce the look and feel of the human skin through three user studies;(2) From a gestural point of view, we explore how gestures naturally performed on skin can be transposed to Skin-On interfaces; (3) From a technical point of view, we explore and discuss different ways of fabricating interfaces that mimic human skin sensitivity and can recognize the gestures observed in the previous study; (4) We assemble the insights of our three exploratory facets to implement a series of Skin-On interfaces and we also contribute by providing a toolkit that enables easy reproduction and fabrication.

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.