Encoding of multi-modal emotional information via personalized skin-integrated wireless facial interface.

Human affects such as emotions, moods, feelings are increasingly being considered as key parameter to enhance the interaction of human with diverse machines and systems. However, their intrinsically abstract and ambiguous nature make it challenging to accurately extract and exploit the emotional information. Here, we develop a multi-modal human emotion recognition system which can efficiently utilize comprehensive emotional information by combining verbal and non-verbal expression data. This system is composed of personalized skin-integrated facial interface (PSiFI) system that is self-powered, facile, stretchable, transparent, featuring a first bidirectional triboelectric strain and vibration sensor enabling us to sense and combine the verbal and non-verbal expression data for the first time. It is fully integrated with a data processing circuit for wireless data transfer allowing real-time emotion recognition to be performed. With the help of machine learning, various human emotion recognition tasks are done accurately in real time even while wearing mask and demonstrated digital concierge application in VR environment.

Digital Mechanical Metamaterial: Encoding Mechanical Information with Graphical Stiffness Pattern for Adaptive Soft Machines.

Inspired by the adaptive features exhibited by biological organisms like the octopus, soft machines that can tune their shape and mechanical properties have shown great potential in applications involving unstructured and continuously changing environments. However, current soft machines are far from achieving the same level of adaptability as their biological counterparts, hampered by limited real-time tunability and severely deficient reprogrammable space of properties and functionalities. As a steppingstone toward fully adaptive soft robots and smart interactive machines, an encodable multifunctional material that uses graphical stiffness patterns is introduced here to in situ program versatile mechanical capabilities without requiring additional infrastructure. Through independently switching the digital binary stiffness states (soft or rigid) of individual constituent units of a simple auxetic structure with elliptical voids, in situ and gradational tunability is demonstrated here in various mechanical qualities such as shape-shifting and -memory, stress-strain response, and Poisson's ratio under compressive load as well as application-oriented functionalities such as tunable and reusable energy absorption and pressure delivery. This digitally programmable material is expected to pave the way toward multienvironment soft robots and interactive machines.

Highly Compressible 3D-Printed Soft Magnetoelastic Sensors for Human-Machine Interfaces.

Incorporating perception into robots or objects holds great potential to revolutionize daily human life. To achieve this, critical factors include the design of an integrable three-dimensional (3D) soft sensor with self-powering capability, a wide working range, and tuneable functionalities. Here, we introduce a highly compressible 3D-printed soft magnetoelastic sensor with a wide strain sensing range. Inspired by the lattice metamaterial, which offers a highly porous structure with tuneable mechanical properties, we realized a remarkably compliant 3D self-powering sensor. Using magnetoelastic composite materials and 3D printing combined with sacrificial molding, a broad design space for constituent materials and structures is investigated, allowing for tuneable mechanical properties and sensor performances. These sensors are successfully integrated with two robotic systems as the robot operation and perception units, enabling robot control and recognition of diverse physical interactions with a user. Overall, we believe that this work represents a cornerstone for compliant 3D self-powered soft sensors, giving impetus to the development of advanced human-machine interfaces.

A soft, self-sensing tensile valve for perceptive soft robots.

Soft inflatable robots are a promising paradigm for applications that benefit from their inherent safety and adaptability. However, for perception, complex connections of rigid electronics both in hardware and software remain the mainstay. Although recent efforts have created soft analogs of individual rigid components, the integration of sensing and control systems is challenging to achieve without compromising the complete softness, form factor, or capabilities. Here, we report a soft self-sensing tensile valve that integrates the functional capabilities of sensors and control valves to directly transform applied tensile strain into distinctive steady-state output pressure states using only a single, constant pressure source. By harnessing a unique mechanism, "helical pinching", we derive physical sharing of both sensing and control valve structures, achieving all-in-one integration in a compact form factor. We demonstrate programmability and applicability of our platform, illustrating a pathway towards fully soft, electronics-free, untethered, and autonomous robotic systems.

3D-printed programmable tensegrity for soft robotics.

Tensegrity structures provide both structural integrity and flexibility through the combination of stiff struts and a network of flexible tendons. These structures exhibit useful properties: high stiffness-to-mass ratio, controllability, reliability, structural flexibility, and large deployment. The integration of smart materials into tensegrity structures would provide additional functionality and may improve existing properties. However, manufacturing approaches that generate multimaterial parts with intricate three-dimensional (3D) shapes suitable for such tensegrities are rare. Furthermore, the structural complexity of tensegrity systems fabricated through conventional means is generally limited because these systems often require manual assembly. Here, we report a simple approach to fabricate tensegrity structures made of smart materials using 3D printing combined with sacrificial molding. Tensegrity structures consisting of monolithic tendon networks based on smart materials supported by struts could be realized without an additional post-assembly process using our approach. By printing tensegrity with coordinated soft and stiff elements, we could use design parameters (such as geometry, topology, density, coordination number, and complexity) to program system-level mechanics in a soft structure. Last, we demonstrated a tensegrity robot capable of walking in any direction and several tensegrity actuators by leveraging smart tendons with magnetic functionality and the programmed mechanics of tensegrity structures. The physical realization of complex tensegrity metamaterials with programmable mechanical components can pave the way toward more algorithmic designs of 3D soft machines.

Reprogrammable Ferromagnetic Domains for Reconfigurable Soft Magnetic Actuators.

Soft magnetic materials have shown promise in diverse applications due to their fast response, remote actuation, and large penetration range for various conditions. Herein, a new soft magnetic composite material capable of reprogramming its magnetization profile without changing intrinsic magnetic properties of embedded magnetic particles or the molecular property of base material is reported. This composite contains magnetic microspheres in an elastomeric matrix, and the magnetic microspheres are composed of ferromagnetic microparticles encapsulated with oligomeric-PEG. By controlling the encapsulating polymer phase transition, the magnetization profiles of the magnetic composite can be rewritten by physically realigning the ferromagnetic particles. Diverse magnetic actuators with reprogrammable magnetization profiles are developed to demonstrate the complete reprogramming of complex magnetization profile.