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Mechanical metamaterials have recently been exploited as an interesting platform for information storing, retrieval and processing, analogous to electronic devices. In this work, we describe the design and fabrication a two-dimensional (2D) multistable metamaterial consisting of building blocks that can be switched between two distinct stable phases, and which are capable of storing binary information analogous to digital bits. By changing the spatial distribution of the phases, we can achieve a variety of different configurations and tunable mechanical properties (both static and dynamic). Moreover, we demonstrate the ability to determine the phase distribution via simple probing of the dynamic properties, to which we refer as mechanical proprioception. Finally, as a simple demonstration of feasibility, we illustrate a strategy for building autonomous kirigami systems that can receive inputs from their environment. This work could bring new insights for the design of mechanical metamaterials with information processing and computing functionalities. This article is part of the theme issue ‘Origami/Kirigami-inspired structures: from fundamentals to applications’. 

Pneumatic soft robots have several advantages, including facile fabrication, versatile deformation modes, and safe human–machine interaction. However, pneumatic soft robots typically rely on mechatronics to interact with their environment, which can limit their form factors and reliability. Researchers have considered how to achieve autonomous behaviors using the principles of mechanical computing and physical intelligence. Herein, modular responsive valves that can autonomously regulate airflow within pneumatic soft robots in response to various environmental stimuli, including light, water, and mechanical forces, are described. By combining multiple types of valves, autonomous logic gates and more advanced logical operations can be realized. Finally, it is demonstrated that responsive valves can be integrated with pneumatic soft robots, allowing autonomous morphing and navigation. This framework provides a strategy for creating autonomous pneumatic robots that can respond to multiple stimuli in their environment. 

Abstract This is a roadmap article with multiple contributors on different aspects of embodying intelligence and computing in the mechanical domain of functional materials and structures. Overall, an IOP roadmap article is a broad, multi-author review with leaders in the field discussing the latest developments, commissioned by the editorial board. The intention here is to cover various topics of adaptive structural and material systems with mechano-intelligence in the overall roadmap, with twelve sections in total. These sections cover topics from materials to devices to systems, such as computational metamaterials, neuromorphic materials, mechanical and material logic, mechanical memory, soft matter computing, physical reservoir computing, wave-based computing, morphological computing, mechanical neural networks, plant-inspired intelligence, pneumatic logic circuits, intelligent robotics, and embodying mechano-intelligence for engineering functionalities via physical computing.  In this paper, we view all the 2-page sections with equal contributions to the overall roadmap article and thus list the authorship on the front page via alphabetical order of their last names.  On the other hand, for each individual section, the authors decide on their own the order of authorship.  

Robots typically interact with their environments via feedback loops consisting of electronic sensors, microcontrollers, and actuators, which can be bulky and complex. Researchers have sought new strategies for achieving autonomous sensing and control in next-generation soft robots. We describe here an electronics-free approach for autonomous control of soft robots, whose compositional and structural features embody the sensing, control, and actuation feedback loop of their soft bodies. Specifically, we design multiple modular control units that are regulated by responsive materials such as liquid crystal elastomers. These modules enable the robot to sense and respond to different external stimuli (light, heat, and solvents), causing autonomous changes to the robot’s trajectory. By combining multiple types of control modules, complex responses can be achieved, such as logical evaluations that require multiple events to occur in the environment before an action is performed. This framework for embodied control offers a new strategy toward autonomous soft robots that operate in uncertain or dynamic environments. 

Fibers capable of generating axial contraction are commonly seen in nature and engineering applications. Despite the broad applications of fiber actuators, it is still very challenging to fabricate fiber actuators with combined large actuation strain, fast response speed, and high power density. Here, we report the fabrication of a liquid crystal elastomer (LCE) microfiber actuators using a facile electrospinning technique. Owing to the extremely small size of the LCE microfibers, they can generate large actuation strain (~60 percent) with a fast response speed (<0.2 second) and a high power density (400 watts per kilogram), resulting from the nematic-isotropic phase transition of liquid crystal mesogens. Moreover, no performance degradation is detected in the LCE microfibers after 10⁶cycles of loading and unloading with the maximum strain of 20 percent at high temperature (90 degree Celsius). The small diameter of the LCE microfiber also results in a self-oscillatory behavior in a steady temperature field. In addition, with a polydopamine coating layer, the actuation of the electrospun LCE microfiber can be precisely and remotely controlled by a near-infrared laser through photothermal effect. Using the electrospun LCE microfiber actuator, we have successfully constructed a microtweezer, a microrobot, and a light-powered microfluidic pump.