A thorough exploration of actuator capabilities can lead to the discovery of new deformation modes. Traditional twisted-string actuator (TSA)-based systems typically utilize only the tensile force generated by string twisting while neglecting the torsional force. As a result, a single actuator often produces only planar deformations such as bending. In this study, we propose the twisted string helical hose (TSHH) mechanism, which innovatively harnesses both tensile and torsional forces to achieve three-dimensional helical deformation through a single input. The TSHH only consists of a motor, string, and hose, all of which are standard commercial components, making the system highly reproducible. We investigated the deformation principle and developed a kinematic model to support parameter optimization. Based on the optimized parameters, we designed a soft gripper capable of gently grasping objects of various shapes and sizes. This work not only demonstrates the promising potential of TSHH in soft robotic applications but also inspires TSA-based systems toward greater design potential.
Tendon driven continuum robots (TDCRs) exhibit extraordinary dexterity and navigability in highly constrained workspaces, for example, in minimally invasive surgery or industrial inspection and repair tasks. The curvilinear nature of these robots, with their extreme diameter-to-length ratios, enables them to dexterously navigate around obstacles in their environment to reach their goals. However, the dynamic properties of these highly deformable mechanisms make them challenging to operate autonomously at high speeds, and the presence of external loads can significantly impact the accuracy of kinematic models. In this work, we present a compact, low-cost, modular tendon robot actuation platform with fast and stable open-loop dynamics. We explore how pretensioned antagonistic pairs can enable repeatable actuation at high bandwidths using our low-cost hardware setup. Secondly, we show how intrinsic sensing using only motor currents and displacement can be used to improve the shape estimate of the robot when manipulating unknown objects at its tip, without any additional sensors. In this work, we show how high-performance continuum robots can be achieved with relatively low-cost components through the mechanical design of the actuation structure and by leveraging existing information available from the actuators in the computational modeling of these systems.
Miniaturization of manipulation systems can be advanced using dielectric elastomer actuators (DEAs). In this work, we present a laser-assisted fabrication method for DEA electrodes using a femtosecond laser, enabling the formation of both macro- and microscale electrode patterns with high precision. The low ablation threshold of carbon nanotubes, combined with the near-infrared transparency of elastomers, allows for selective removal of the conductive layer with minimal thermal damage to the elastomer. This single-step laser ablation process was employed to fabricate the actuation layers of DEAs, incorporating both the electrode geometry and integrated conductive wire connections. Based on this approach, we constructed a miniaturized cone-shaped actuator composed of two stacked actuation layers separated by a central cylindrical pin, resulting in simultaneous actuation and self-sensing. We further introduced cone-shaped DEA arrays (up to 4 × 4) to realize scalable smart surface manipulation. Their capabilities have been demonstrated through two representative applications: (i) guiding a steel ball through a maze by dynamically tilting the maze surface by the DEA arrays and (ii) steering a reflected laser beam by controlling the orientation of a mirror via the DEA array. These demonstrations highlight the scalability and multifunctionality of laser-fabricated DEA arrays for next-generation soft robotic interactive surface systems.
Touch is often attributed to the skin, but what if hard structures above the contact surface play a larger role than we thought? While most haptic research focuses on soft tissue, we explore how the fingernail and distal phalanx bone contribute to texture perception, combining human exploration data, finite element analysis (FEA), and experiments with a soft robotic fingertip equipped with Hall-effect sensors. Analysis of human exploration revealed a predominant use of lateral movements, which informed the robotic and simulation procedures. FEA showed that adding rigid elements altered internal stress patterns within the soft tissue during lateral sweeping, increasing strain variability in regions relevant for sensing. Lateral sweeping consistently produced higher strain variability than longitudinal motion, with Shannon entropy ratios up to 1.96 across probe regions. Robotic texture classification experiments quantified these effects: Adding only a nail improved discrimination accuracy by 22%, adding only a bone by 51%, and integrating both features increased performance by more than 88% compared to the soft-only model. These results demonstrate that the nail and distal phalanx bone provide complementary mechanical benefits that enhance texture-related signal richness and can improve the efficacy of soft tactile systems. This study can lead to a design principle for soft robotic tactile systems in prosthetics, surgical robotics, and dexterous manipulation where sensing performance must be maximized without increasing sensor complexity.
As soft robotics gains popularity, novel toolkits and educational systems for soft robotics are emerging. These endeavors focus mainly on teaching the programming or fabrication of soft robots, actuators, and sensors. However, as soft robotics moves toward electronics- and software-free control, it becomes essential to gain hands-on experience in soft fluidic circuit building and physical control. We bridge this gap in the educational landscape through the Soft Circuits Toolkit, a modular system of completely soft components that enables the creation and teaching of fluidic circuits, representing fluidic analogies of components such as resistors, capacitors, diodes, and transistors. The toolkit went through two design iterations and four user tests, which ultimately resulted in the toolkit presented in this article, consisting of 17 component types, a manual, and a companion website. User testing across various dimensions, including user experience, with technical university students ( N = 25) indicates that our toolkit is engaging, easy to learn, and promotes problem-solving behaviors. Based on these results, we believe that our kit can support technical universities in teaching about fluidic circuit building and physical control of soft robotics. All fabrication files for this toolkit have been made available open source.
