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Creativity and style in music playing originates from constraints and imperfect interactions between instruments and players. Digital and robotic systems have so far been unable to capture this naturalistic playing. Whether as an additional tool for musicians, function restoration with prosthetics, or artificial intelligence-powered systems, the physical embodiment and interactions generated are critical for expression and connection with an audience. We introduce the parametric hand, which serves as a platform to explore the generation of diverse interactions for the stylistic playing of both pianos and guitars. The hand’s anatomical design and non-linear actuation are exploitable with simple kinematic modeling and synergistic actuation. This enables the modulation of two degrees of freedom for piano chord playing and guitar strumming with up to 6.6 times the variation in the signal amplitude. When only varying hand stiffness properties, we achieve capabilities similar to the variation exhibited in human strumming. Finally, we demonstrate the exploitability of behaviours with the rapid programming of posture and stiffness for sequential instrument playing, including guitar pick grasping. In summary, we highlight the utility of embodied intelligence in musical instrument playing through interactive behavioural diversity, as well as the ability to exploit behaviours over this diversity through designed behavioural robustness and synergistic actuation.

Abstract Additive manufacturing, or 3D printing attracts growing attention as a promising method for creating functionally graded materials. Fused deposition modeling (FDM) is widely available, but due to its simple process, creating spatial gradation of diverse properties using FDM is challenging. Here, we present a 3D printed digital material filament that is structured towards 3D printing of functional gradients, utilizing only a readily available FDM printer and filaments. The DM filament consists of multiple base materials combined with specific concentrations and distributions, which are FDM printed. When the DM filament is supplied to the same printer, its constituent materials are homogeneously blended during extrusion, resulting in the desired properties in the final structure. This enables spatial programming of material properties in extreme variations, including mechanical strength, electrical conductivity, and color, which are otherwise impossible to achieve with traditional FDMs. Our approach can be readily adopted to any standard FDM printer, enabling low-cost production of functional gradients.

This paper proposes a low-power design method and a low-noise phase offset calibration technique for a gated ring-oscillator time-to-digital converter (GRO-TDC), which normally consumes a large percentage of most all-digital phase-locked loop (ADPLL) power. A single coarse counter logic structure along with time-interleaved even/odd paths significantly reduces the complexity and speed of the TDC logic. The proposed TDC consumes only 0.44 to 24 mW for 0.077 to 24.42 ns of detection range. The multi-path GRO accelerates the oscillation speed and achieves approximately 6.2 ps of time resolution. The GRO-TDC shows –1.43 to 1.35 least-significant bits (LSB) of differential non-linearity (DNL) and –1.32 to 1.96 LSB of integral non-linearity (INL) over a 11-bit dynamic range (DR). The entire ADPLL including the proposed TDC has been fabricated in a 65 nm CMOS process and occupies 0.67 mm2 of active area. The prototype ADPLL consumes 12.22 mW from 1.2 V supply and the TDC consumes only 0.65 mW for a 50-phase offset code. A modified integrating structure in the subsequent digital loop filter (DLF) has been developed to mitigate dithering noise on $V_{ctrl}$ code and the measured reference spur is –69.38 dBc at 3.6 GHz center frequency. The tuning range of the implemented ADPLL is 3.5 to 4.7 GHz by using 2-bit band switching and 5-bit coarse control, while maintaining low- $K_{DCO}$ values to suppress in-band quantization noise. The measured root-mean-square (RMS) jitter is 0.94 ps and 0.99 ps at 3.6 GHz integer-mode and 3.60743 GHz fractional-mode respectively.

Adaptive and extreme changes in shape and configuration are the functional and morphological uniqueness of soft robots, but existing design approaches still rely on the predefined coordination of their “muscle” and “nerve” functions to produce such behaviors. Herein, a strategy is introduced for building modular soft machines that can be innervated in ways that conform to their body extension or shape changes, based on modular soft electronics. The development of soft electronic adhesive interlocking (SEAL) technology allows for instant, robust, and repeatable integration of soft electronic modules that can “innervate” and activate modular soft actuators and machines in a reconfigurable manner. Demonstrations of soft robotic tentacles and their grasping capability show that the robot function can be adapted to or reconfigured within the body with a length extended more than 10 times. The modular strategy presented herein can offer a unique promise to build up future robots with dynamic, reconfigurable functions.

Soft fluidic actuators produce continuous and life‐like motions that are intrinsically safe, but current designs are not yet mature enough to enable large deployment with high force and low‐cost fabrication methods. Herein, soft fluidic actuators with two superimposed origami architectures are reported. Driven by a fluid input, the presented dual‐origami soft actuators produce quasisequential deployment and bending motion that is guided by unsymmetric unfolding of low‐stretchable origami components. The dominance between the deployment and bending can be shifted by varying the unfolding behavior, enabling preprogramming of the motion. The proposed origami‐inspired soft actuators are directly fabricated by low‐cost fused deposition modeling 3D printing and subjected to heat treatment postprocessing to enhance the fluid sealing performance. Finally, soft gripper applications are presented and they successfully demonstrate gripping tasks where each requires strength, delicacy, precision, and dexterity. The dual‐origami approach offers a design guidance for soft robots to embody grow‐and‐retract motion with a small initial form factor, promising for applications in next‐generation soft robotic systems. An interactive preprint version of the article can be found here: https://www.authorea.com/doi/full/10.22541/au.163698906.68661340.

Touch technology holds potential for the development of smartphones and touchscreens, yet the conventional devices are usually built on separate pressure and location sensing units. Kim et al. show a flexible and transparent touch sensor capable of mapping the position and pressure at the same time.

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.

Abstract Background Soft tissue balancing which is above all most important factor of total knee arthroplasty, has been performed by subjective methods. Recently objective orthosensor has been developed for compartment pressure measurement. The purpose of this study was: (1) to quantify the compartment pressure of the joint throughout the range of motion during TKA using orthosensor, (2) to determine the usefulness of orthosensor by analyzing correlation between the pressure in both compartment with initial trial and after final implantation, and (3) to evaluate the types and effectiveness of additional ligament balancing procedures to compartment pressure. Methods Eighty-four patients underwent total knee arthroplasty (TKA) using VERASENSE Knee System. TKA was performed by measured resection and modified gap balance technique. Compartment pressure was recorded on full extension, 30°, 60°, 90° and full flexion at initial (INI), after each additional procedure, and after final (FIN) implantation. “Balanced” knees were defined as when the compartment pressure difference was less than 15 pounds. Results Thirty patients (35.7%) showed balanced knee initially and 79 patients (94.0%) showed balance after final implantation. The proportion of balanced knee after initial bony resection, modified gap balancing TKAs showed significantly higher proportion than measured resection TKAs (P = 0.004) On both compartment, the pressure was generally decreased throughout the range of motion. Linear correlation on both compartment showed statistically significant throughout the range on motion, with higher correlation value on the lateral compartment. Total 66 additional ligament balancing procedures were performed. Conclusion Using orthosensor, we could obtain 94% quantified balance knee, consequently. And between the techniques, measured resection TKA showed less balanced knee and also required more additional procedures compared to modified gap balancing TKA. Furthermore, with the acquired quantified data during appropriate ligament balancing, the surgeon could eventually reduce the complications associated with soft tissue imbalance in the future.

The size of a device and its adaptability to human properties are important factors in developing a wearable device. In wearable robot research, therefore, soft materials and tendon transmissions have been utilized to make robots compact and adaptable to the human body. However, when used for wearable robots, these methods sometimes cause uncertainties that originate from elongation of the soft material or from undefined human properties. In this research, to consider these uncertainties, we propose a data-driven method that identifies both kinematic and stiffness parameters using tension and wire stroke of the actuators. Through kinematic identification, a method is proposed to find the exact joint position as a function of the joint angle. Through stiffness identification, the relationship between the actuation force and the joint angle is obtained using Gaussian Process Regression (GPR). As a result, by applying the proposed method to a specific robot, the research outlined in this paper verifies how the proposed method can be used in wearable robot applications. This work examines a novel wearable robot named Exo-Index, which assists a human’s index finger through the use of three actuators. The proposed identification methods enable control of the wearable robot to result in appropriate postures for grasping objects of different shapes and sizes.

Compensating the weight of human limbs is important in reducing muscle fatigue experienced by manual laborers. In this study, a compact and lightweight soft wearable weight support device was developed and evaluated. The device supports gravitational force on the shoulder at any arm posture, although there are some limitations in its assistive performance. The device actuator consists of a cam-rod structure, a tendon-driven mechanism, and a rubber band. The desired assistive torque is translated to the shoulder joint along a tendon routing structure. Device performance was evaluated by measuring muscle activation in with-assist and without-assist conditions. Muscle activation on the deltoid was measured by surface electromyography. An experimental protocol consisting of a series of exercises was executed with six healthy subjects. The subjects raised and lowered their arm from 0 to 100 degrees for 30 times under eight conditions, which were combined with-assist and without-assist conditions, and holding the horizontal angle of the arm at 0, 30, 60, or 90 degrees against the sagittal plane. Surface electromyography data were pre-processed and analyzed using a root mean square method. When muscle fatigue occurs, the root mean square of the surface electromyography increases nonlinearly. This was calculated using the standard deviation of the root mean square. Three of six subjects showed decreased variation of the root mean square between the exercises in the with-assist condition. One subject's result was significantly reduced (by about 57.6%) in the with-assist condition. In contrast, two subjects did not show significant difference between measurements taken in the with-assist and without-assist conditions. One subject was dropped from the experiment because the device did not fit the subject's body. In conclusion, the effectiveness of the soft wearable weight support device in supporting shoulder movements was verified through the decreased variation of muscle activation.

This paper introduces SNUMAX, the grand winner of the RoboSoft Grand Challenge. SNUMAX was built to complete all the tasks of the challenge. Completing these tasks required robotic compliant components that could adapt to variable situations and environments and generate enough stiffness to maintain performance. SNUMAX has three key components: transformable origami wheels, a polymer-based variable stiffness manipulator, and an adaptive caging gripper. This paper describes the design of these components and how they worked together to allow the robot to perform the contest’s navigation and manipulation tasks.