The inherent compliant nature of soft robots can offer remarkable advantages over their rigid counterparts in terms of safety to human users and adaptability in unstructured environments. However, this feature also magnifies the complexity of their bodies, rendering their proprioception, and hence their control, extremely challenging. Given this intricacy, machine learning is a potent candidate for extracting proprioceptive insights from sensor data due to its proven capabilities in tackling analogous issues in computer vision (CV) and natural language processing (NLP). Recently, key aspects of soft robot proprioception have been addressed via learning-based techniques, but most of these are rooted in the supervised learning (SL) paradigm. This typically requires collecting a large number of costly annotated training samples, thereby constraining its widespread and speedy adoption in real-world applications. To mitigate this limitation, we propose a self-SL framework for soft robot proprioception. Our method utilizes vast unannotated data for network pretraining by self-SL. Then, the pretrained model is fine-tuned with a limited set of annotated samples by SL. We validate the proposed method's efficacy on a high-resolution 3-D morphological reconstruction task using a publicly available dataset. Remarkably, our approach is shown to necessitate only about 1/20 of annotated samples to achieve better performance than the fully supervised method.

With the increasing demand for human-computer interaction (HCI), flexible wearable gloves have emerged as a promising solution in virtual reality, medical rehabilitation, and industrial automation. However, the current technology still has problems like insufficient sensitivity and limited durability, which hinder its wide application. This paper presents a highly sensitive, modular, and flexible capacitive sensor based on line-shaped electrodes and liquid metal (EGaIn), integrated into a sensor module tailored to the human hand’s anatomy. The proposed system independently captures bending information from each finger joint, while additional measurements between adjacent fingers enable the recording of subtle variations in inter-finger spacing. This design enables accurate gesture recognition and dynamic hand morphological reconstruction of complex movements using point clouds. Experimental results demonstrate that our classifier based on Convolution Neural Network (CNN) and Multilayer Perceptron (MLP) achieves an accuracy of 99.15% across 30 gestures. Meanwhile, a transformer-based Deep Neural Network (DNN) accurately reconstructs dynamic hand shapes with an Average Distance (AD) of 2.076±3.231 mm, with the reconstruction accuracy at individual key points surpassing SOTA benchmarks by 9.7% to 64.9%. The proposed glove shows excellent accuracy, robustness and scalability in gesture recognition and hand reconstruction, making it a promising solution for next-generation HCI systems.

Hyper-redundant tendon-driven manipulators offer greater flexibility and compliance over traditional manipulators. A common way of controlling such manipulators relies on adjusting tendon lengths, which is an accessible control parameter. This approach works well when the kinematic configuration is representative of the real operational conditions. However, when dealing with manipulators of larger size subject to gravity, it becomes necessary to solve a static force problem, using tendon force as the input and employing a mapping from the configuration space to retrieve tendon length. Alternatively, measurements of the manipulator posture can be used to iteratively adjust tendon lengths to achieve a desired posture. Hence, either tension measurement or state estimation of the manipulator are required, both of which are not always accurately available. Here, we propose a solution by reconciling cables tension and length as the input for the solution of the system forward statics. We develop a screw-based formulation for a tendon-driven, multi-segment, hyper-redundant manipulator with elastic joints and introduce a forward statics iterative solution method that equivalently makes use of either tendon length or tension as the input. This strategy is experimentally validated using a traditional tension input first, subsequently showing the efficacy of the method when exclusively tendon lengths are used. The results confirm the possibility to perform open-loop control in static conditions using a kinematic input only, thus bypassing some of the practical problems with tension measurement and state estimation of hyper-redundant systems.

Variable Stiffness Mechanisms (VSM) are becoming ubiquitous in mechatronics given the benefit they provide in terms of safety and performance. Despite these assets, VSMs remain fairly complex mechanical devices lacking in compactness, ease of manufacturing and accessibility. In addition, the scarcity of commercially available VSMs requires that such systems are mostly designed in-house. We propose a new type of VSM that improves on the pre-existing Jack Spring concept by making it more compact and robust. The new concept, which we refer to as the Compact Modifier of Active Coils (C-MAC) mechanism, is specifically designed to be manufactured through a monolithic 3D print. This approach enables to modify a minimal set of design features, namely the spring diameter and the coil diameter, to achieve the desired range of stiffness variation. We test the proposed design on six configurations; these show hysteretic energy losses no larger than 35% over the stiffness variation and confirm stiffness to scale according to theory. Stiffness ranging from 0.15 N/mm to 1.02N/mm were measured for an overall device length of 140 mm, including a maximal stroke length of 22 mm. The results confirm excellent scalability and manufacturability of the proposed design, providing a versatile mechanism for fast prototyping and the development of entire 3D printed robotic systems embedding variable stiffness capabilities.

Stability and reliable operation under a spectrum of environmental conditions is still an open challenge for soft and continuum style manipulators. The inability to carry sufficient load and effectively reject external disturbances are two drawbacks which limit the scale of continuum designs, preventing widespread adoption of this technology. To tackle these problems, this work details the design and experimental testing of a modular, tendon driven bead-style continuum manipulator with tunable stiffness. By embedding the ability to independently control the stiffness of distinct sections of the structure, the manipulator can regulate it’s posture under greater loads of up to 1kg at the end-effector, with reference to the flexible state. Likewise, an internal routing scheme vastly improves the stability of the proximal segment when operating the distal segment, reducing deviations by at least 70.11%. Operation is validated when gravity is both tangential and perpendicular to the manipulator backbone, a feature uncommon in previous designs. The findings presented in this work are key to the development of larger scale continuum designs, demonstrating that flexibility and tip stability under loading can co-exist without compromise.

Operating in the near-vicinity of marine energy devices poses significant challenges to the control of underwater vehicles, predominantly due to the presence of large magnitude wave disturbances causing hazardous state perturbations. Approaches to tackle this problem have varied, but one promising solution is to adopt predictive control methods. Given the predictable nature of ocean waves, the potential exists to incorporate disturbance estimations directly within the plant model; this requires inclusion of a wave predictor to provide online preview information. To this end, this paper presents a Non-linear Model Predictive Controller with an integrated Deterministic Sea Wave Predictor for trajectory tracking of underwater vehicles. State information is obtained through an Extended Kalman Filter, forming a complete closed-loop strategy and facilitating online wave load estimations. The strategy is compared to a similar feed-forward disturbance mitigation scheme, showing mean performance improvements of 51% in positional error and 44.5% in attitude error. The preliminary results presented here provide strong evidence of the proposed method's high potential to effectively mitigate disturbances, facilitating accurate tracking performance even in the presence of high wave loading.

Prokaryotic flagellum is considered as the only known example of a biological “wheel,” a system capable of converting the action of rotatory actuator into a continuous propulsive force. For this reason, flagella are an interesting case study in soft robotics and they represent an appealing source of inspiration for the design of underwater robots. A great number of flagellum-inspired devices exists, but these are all characterized by a size ranging in the micrometer scale and mostly realized with rigid materials. Here, we present the design and development of a novel generation of macroscale underwater propellers that draw their inspiration from flagellated organisms. Through a simple rotatory actuation and exploiting the capability of the soft material to store energy when interacting with the surrounding fluid, the propellers attain different helical shapes that generate a propulsive thrust. A theoretical model is presented, accurately describing and predicting the kinematic and the propulsive capabilities of the proposed solution. Different experimental trials are presented to validate the accuracy of the model and to investigate the performance of the proposed design. Finally, an underwater robot prototype propelled by four flagellar modules is presented.

Shallow water environments pose daunting scenarios for the operation of Unmanned Underwater Vehicles (UUVs), due to significantly larger wave disturbances being present in comparison to a typical deep sea situation. Performing inspection and maintenance tasks at close quarters in these conditions requires reliable control methods robust to external disturbances, allowing accurate position and attitude control, an aspect which classical control methods are often lacking. Improved performance can be achieved through predictive control methods, however, these require accurate and time-efficient estimations of the hydrodynamic forces produced by the immediate ocean environment around the vehicle. Considering this, we present a low-order model for faster-than-real time estimation of the wave-induced hydrodynamic forces acting on a submerged vehicle in various sea state conditions. The model is thoroughly corroborated by experimental tests, performed using a Remotely Operated Vehicle (ROV) situated at shallow depth whilst subjected to realistic sea wave disturbances. Validation between simulations and the collected experimental data showed a maximum normalised mean error deviation of 0.16 and 0.27 for surge and heave forces respectively, and 0.34 for the pitching moment. This empirical evidence demonstrates that accurate predictions of wave-generated forces can be produced through low-order models at a speed suitable for incorporation within predictive control architectures.

Flagellated micro-organism are regarded as excellent swimmers within their size scales. This, along with the simplicity of their actuation and the richness of their dynamics makes them a valuable source of inspiration to design continuum, self-propelled underwater robots. Here we introduce a soft, flagellum-inspired system which exploits the compliance of its own body to passively attain a range of geometrical configurations from the interaction with the surrounding fluid. The spontaneous formation of stable helical waves along the length of the flagellum is responsible for the generation of positive net thrust. We investigate the relationship between actuation frequency and material elasticity in determining the steady-state configuration of the system and its thrust output. This is ultimately used to perform a parameter identification procedure of an elastodynamic model aimed at investigating the scaling laws in the propulsion of flagellated robots.

This paper reports on the development and validation of the elastodynamics model of an innovative underwater soft-bodied robot inspired by cephalopods. The vehicle, for which the model is devised, is propelled by a discontinuous activation routine which entails the collapse of an elastic shell via cable transmission and its following passive re-inflation under the action of the elastic energy stored in the shell walls. Activation routine and thrust characterization have been determined to depend massively on the capability of the shell to elastically return to its unstrained state, hence an accurate description of the dynamics of the shell during all stages of actuation and at various degrees of deformation is essential. The model, based on a geometrically exact Cosserat theory, is validated against measurement achieved from an ad-hoc experimental apparatus, bringing evidence of its aptness at capturing the key parameters of the system. Eventually the model is employed for simulating a proper propulsion routine in water demonstrating that, upon suitable parametrization of the internal and external hydrodynamics, it can reliably be employed for the realistic quantitative characterization of the cephalopod-inspired robot.

This paper reports on the development of a new kind of unmanned underwater vehicle which draws inspiration from cephalopods both in terms of morphology and swimming routine. The robot developed here is the first in its kind, being a soft aquatic robot which travels in water by pulsed-jet propulsion. The general design principles of this innovative kind of underwater robot are illustrated and a first prototype is built and tested. The experiments demonstrate an inverse correlation between the frequency of pulsation and the speed of the robot. A mathematical model which associates the kinematics of the pulsating routine to the dynamics of the swimming is devised and compared with the experiments in order to better investigate the interplay of the various design parameters.