Publications

This work presents a novel tactile perception-based method, named T-NT, for performing the needle-threading task, an application of deformable linear object (DLO) manipulation. This task is divided into two main stages: Tail-end Finding and Tail-end Insertion. In the first stage, the agent traces the contour of the thread twice using vision-based tactile sensors mounted on the gripper fingers. The two-run tracing is to locate the tail-end of the thread. In the second stage, it employs a tactile-guided reinforcement learning (RL) model to drive the robot to insert the thread into the target needle eyelet. The RL model is trained in a Unity-based simulated environment. The simulation environment supports tactile rendering which can produce realistic tactile images and thread modeling. During insertion, the position of the poke point and the center of the eyelet are obtained through a pre-trained segmentation model, Grounded-SAM, which predicts the masks for both the needle eye and thread imprints. These positions are then fed into the reinforcement learning model, aiding in a smoother transition to real-world applications. Extensive experiments on real robots are conducted to demonstrate the efficacy of our method. More experiments and videos can be found in the supplementary materials and on the website: https://sites.google.com/view/tac-needlethreading.

Matrix-addressed flexible pressure sensors, being able to accurately measure both local contact force and spatial distribution, are pursued for many electronic skin applications. One key issue to be addressed is that the local force being applied onto the target areas may be passed to the neighboring pixels through deformation of the touched top electrode layer. It causes significant signal crosstalk and also loss of measurement accuracy. A new top electrode layer structure is proposed with the development of processes for matrix-addressed pressure sensor systems. It is composed of a patterned layer of high Young's modulus and a low-modulus encapsulation layer. The former is able to sustain a relatively high processing temperature for forming reliable and high-density electrical connections.The latter is to protect the patterned layer while having low Young's modulus to minimize the spreading of local deformation at the pressed pixel to the surrounding ones. A 10 × 10 matrix-addressed flexible capacitive pressure sensor system is constructed to verify this design, showing effective suppression of the pixel-to-pixel signal crosstalk and improvement of measurement accuracy. The flexible pressure sensor system is integrated onto a prosthetic hand, showing capabilities of differentiating details of massage balls.

This review provides an overview of stimuli-responsive functional materials and their applications in soft robotics.

No abstract is provided for this article.

No abstract is provided for this article.

Artificial muscles are promising in soft exoskeletons, locomotion robots, and operation machines. However, their performance in contraction ratio, output force, and dynamic response is often imbalanced and limited by materials, structures, or actuation principles. We present lightweight, high-contraction ratio, high-output force, and positive pressure-driven X-crossing pneumatic artificial muscles (X-PAMs). Unlike PAMs, our X-PAMs harness the X-crossing mechanism to directly convert linear motion along the actuator axis, achieving an unprecedented 92.9% contraction ratio and an output force of 207.9 Newtons per kilogram per kilopascal with excellent dynamic properties, such as strain rate (1603.0% per second), specific power (5.7 kilowatts per kilogram), and work density (842.9 kilojoules per meter cubed). These properties can overcome the slow actuation of conventional PAMs, providing robotic elbow, jumping robot, and lightweight gripper with fast, powerful performance. The robust design of X-PAMs withstands extreme environments, including high-temperature, underwater, and long-duration actuation, while being scalable to parallel, asymmetric, and ring-shaped configurations for potential applications.

Shape reconfiguration is ubiquitous in nature and widely used in many applications such as soft robotics, metamaterials, energy absorption and tissue engineering. Shape reconfigurable soft actuators, due to their ability to adapt and adjust in complex and unpredictable working environment, have been designed by the use of various delicate structures and active materials. However, soft actuators that exhibit reconfigurable helical deformation have not been proposed; they have the advantage of integrating both bending and twisting actuations in one deformation mode. In this work, we present a thermal-induced shape reconfigurable soft actuator that shows reversible actuations with vastly shape differences under thermal stimulus. It exhibits helical deformation at lower temperature and mainly in-plane bending at relatively higher temperature. The reversible shape transition is controlled by a thermal stimulus that changes the anisotropy of the structure, which consists of shape memory polymer fibers embedded in a homogeneous elastic matrix. A theoretical model is proposed based on the minimum potential energy that incorporates the thermomechanical behavior of the shape memory polymer fibers. Experiments are conducted and the results agree well with the theoretical modeling. Using the theoretical model, we establish design principles for reconfigurable soft actuators whose functional response is programmable given the architecture and external stimulus. A six-handed helical soft actuator, constructed to demonstrate its programmable deformation, is utilized to catch a living fish in water.

Touch sensing has a central role in robotic grasping and emerging human–machine interfaces for robot‐assisted prosthetics. Although advancements in soft conductive polymers have promoted the creation of diverse pressure sensors, these sensors are difficult to be employed as touch skins for robotics and prostheses due to their limited sensitivity, narrow pressure range, and complex structure and fabrication process. Here, a highly sensitive and robust soft touch skin is presented with ultracapacitive sensing that combines ionic hydrogels with commercially available conductive fabrics. Prototypical designs of the capacitive sensors through facile manufacturing methods are introduced and a high sensitivity up to 1.5 kPa −1 (≈44 times higher than conventional parallel‐plate capacitive counterparts), a broad pressure detection range of over four orders of magnitudes (≈35 Pa to 330 kPa), ultrahigh baseline of capacitance, fast response time (≈18 ms), and good repeatability are demonstrated. Ionogel skins composed of an array of cutaneous mechanoreceptors capable of monitoring various physiological signals and shape detection are further developed. The touch skin can be integrated within a soft bionic hand and provide an industrial robot and an amputee with robust tactile feedback when handling delicate objects, illustrating its potential applications in next‐generation human‐in‐the‐loop robotic systems with tactile sensing.

Nature has been inspiring scientists to fabricate impact protective materials for applications in various aspects. However, it is still challenging to integrate flexible, stiffness-changeable, and protective properties into a single polymer, although these merits are of great interest in many burgeoning areas. Herein, we report an impact-protective supramolecular polymeric material (SPM) with unique impact-hardening and reversible stiffness-switching characteristics by mimicking sea cucumber dermis. The emergence of softness–stiffness switchability and subsequent protective properties relies on the dynamic aggregation of the nanoscale hard segments in soft transient polymeric networks modulated by quadruple H-bonding. As such, we demonstrate that our SPM could efficiently reduce the impact force and increase the buffer time of the impact. Importantly, we elucidate the underlying mechanism behind the impact hardening and energy dissipation in our SPM. Based on these findings, we fabricate impact- and puncture-resistant demos to show the potential of our SPM for protective applications.

No abstract is provided for this article.

Artificial synaptic devices that can be stretched similar to those appearing in soft-bodied animals, such as earthworms, could be seamlessly integrated onto soft machines toward enabled neurological functions. Here, we report a stretchable synaptic transistor fully based on elastomeric electronic materials, which exhibits a full set of synaptic characteristics. These characteristics retained even the rubbery synapse that is stretched by 50%. By implementing stretchable synaptic transistor with mechanoreceptor in an array format, we developed a deformable sensory skin, where the mechanoreceptors interface the external stimulations and generate presynaptic pulses and then the synaptic transistors render postsynaptic potentials. Furthermore, we demonstrated a soft adaptive neurorobot that is able to perform adaptive locomotion based on robotic memory in a programmable manner upon physically tapping the skin. Our rubbery synaptic transistor and neurologically integrated devices pave the way toward enabled neurological functions in soft machines and other applications.