In this paper, we propose a multimodal flexible sensory interface for interactively teaching soft robots to perform skilled locomotion using bare human hands. First, we develop a flexible bimodal smart skin (FBSS) based on triboelectric nanogenerator and liquid metal sensing that can perform simultaneous tactile and touchless sensing and distinguish these two modes in real time. With the FBSS, soft robots can react on their own to tactile and touchless stimuli. We then propose a distance control method that enabled humans to teach soft robots movements via bare hand-eye coordination. The results showed that participants can effectively teach a self-reacting soft continuum manipulator complex motions in three-dimensional space through a “shifting sensors and teaching” method within just a few minutes. The soft manipulator can repeat the human-taught motions and replay them at different speeds. Finally, we demonstrate that humans can easily teach the soft manipulator to complete specific tasks such as completing a pen-and-paper maze, taking a throat swab, and crossing a barrier to grasp an object. We envision that this user-friendly, non-programmable teaching method based on flexible multimodal sensory interfaces could broadly expand the domains in which humans interact with and utilize soft robots.

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and structure, self-learning, self-healing, and self-decision-making into physical bodies of the biological or robotic agents. Physical intelligence can also be generated during the interaction between the agent's body and the environment over time. The previous focus of intelligent robots is primarily focused on the computational intelligence. As a new paradigm, physical intelligence is expected to boost intelligent robots in real-world applications.Soft robots commonly use soft stimuli-responsive materials and intelligent structures and maintain high stretchability and considerable deformation, therefore, have intrinsic environmental conformable physical property. Thus, soft robots are essential platforms for testing hypothesis of physical intelligence in nature. This review mainly focused on three key physical intelligence elements encoded in the natural organisms' bodies: material, structure, and morphology. Through bio-inspired design, smart soft materials, smart soft structures, and adaptable morphologies can be integrated into the robot's body, thus introducing bio-inspired physical intelligence into the robots. By integrating bio-inspired physical intelligence, soft robots could reduce the cost of control, improve the response speed of the systems, enhance the robustness of robots in extreme environments, and embed intelligence into the micro-and small-scale robots. The research on bio-inspired physical intelligence A c c e p t e d https://engine.scichina.com/doi/10.1360/TB-2021-1217 may also promote the multi-disciplinary collaboration of biology, robotics, materials science, chemistry, computer science, etc.In this review, we first describe the characteristics and principles of physical intelligence in natural organisms' material, structure, and morphology. Then we introduce the purposes and related key technologies and methods of realizing bio-inspired physical intelligence of soft robots. Furthermore, we enumerate the typical applications of bio-inspired physical intelligence of soft robots. Finally, we highlight the trends and challenges of bio-inspired physical intelligence of soft robots in the future.The research on bio-inspired physical intelligence for soft robots is still in the primary stage. There are several critical questions and challenges to be addressed. First, researchers should investigate the basic principles of physical intelligence in biomechanics and then apply the basic principles to guide the development of soft robots. Second, intelligent materials need to meet the challenges of fully integrating sensing, actuation, memory, computation, and communication in soft robots. To this end, an interesting approach is to use stimulus-responsive materials as the basic building blocks to rationally design and integrate different functions into a single composite material. Third, smart structures, like mechanical metamaterials can be a promising research direction in the field of intelligent structures for soft robots. Aided by artificial intelligence and 3D printing, mechanical metamateria...

Finlets have a unique overhanging structure at the back, similar to a flag. They are located between the dorsal/anal fin and the caudal fin on the sides of the body. Until now, the sensing ability of finlets has not been well understood. In this paper, we design and manufacture a biomimetic soft robotic finlet (48.5 mm long, 30 mm high) with mechanosensation based on printed stretchable liquid metal sensors. The robotic finlet's posterior fin ray can achieve side-to-side movement orthogonal to the anterior fin ray. A flow sensor encapsulating a liquid metal sensor network enables the biomimetic finlets to sense the direction and flow intensity. The stretchable liquid metal sensors mounted on micro-actuators are utilized to perceive the swing motion of the fin ray. We found that the finlet prototype can sense the flapping amplitudes and frequency of the fin ray. The membrane between the two orthogonal fin rays can amplify the sensor output. Our results indicate that the overhanging structure endows the biomimetic finlet with the ability to sense external stimuli from stream-wise, lateral and vertical directions. We further demonstrate, through digital particle image velocimetry experiments, that the finlet can detect a Kármán vortex street. This study lays the foundations for exploring the environmental perception of biological fish fins and provides a new approach for the perception of complex flow environments by future underwater robots.

Miniature soft sensors are crucial for the perception of soft robots. Although centimeter-scale sensors have been well developed, very few works addressed millimeter-scale, three-dimensional-shaped soft sensors capable of measuring multi-axis forces. In this work, we developed a millimeter-scale (overall size of 6 mm × 11 mm × 11 mm) soft sensor based on liquid metal printing technology and self-folding origami parallel mechanism. The origami design of the sensor enables the soft sensor to be manufactured within the plane and then fold into a three-dimensional shape. Furthermore, the parallel mechanism allows the sensor to rotate along two orthogonal axes. We showed that the soft sensor can be self-folded (took 17 s) using a shape-memory polymer and magnets. The results also showed that the sensor prototype can reach a deformation of up to 20 mm at the tip. The sensor can realize a measurement of external loads in six directions. We also showed that the soft sensor enables underwater sensing with a minimum sensitivity of 20 mm/s water flow. This work may provide a new manufacturing method and insight into future millimeter-scale soft sensors for bio-inspired robots.

In this article, a lightweight, untethered wearable adhesive glove (total weight 72 g) is developed to empower the human hand with adhesive gripping. A textile glove comprises ionogels with embedded flexible heaters and a wireless electronic control module. The thermoresponsive ionogel with switchable adhesion achieves a switching ratio of 4.9 after heating 4.4 s under 4 V voltage, enabling on‐demand object pickup and release. As demonstration examples, the wearable adhesive glove is shown to grasp multiple objects with single‐finger adhesion and manipulate card with two‐finger adhesion. It is shown in the experiments that the wearable adhesive glove can expand the gripping modes and enhance the gripping ability of the human hand by giving it a new degree of freedom of adhesion.

The evaluation of soil quality can provide new insights into the sustainable management of forests. This study investigated the effects of three types of forest management intensities (non-management (CK), extensive management (EM), and intensive management (IM)), and five management durations (0, 3, 8, 15, and 20 years) on the soil quality of a Carya dabieshanensis forest. Further, minimum data sets (MDS) and optimized minimum data sets (OMDS) were established to evaluate the soil quality index (SQI). A total of 20 soil indicators representing its physical, chemical, and biological properties were measured for the 0–30 cm layer. Using one-way ANOVA and principal component analysis (PCA), the total data set (TDS), the minimum data set (MDS), and optimized minimum data set (OMDS) were established. The MDS and OMDS contained three (alkali hydrolyzed nitrogen (AN), soil microbial biomass nitrogen (SMBN), and pH) and four (total phosphorus (TP), soil organic carbon (SOC), AN, and bulk density (BD)) soil indicators, respectively. The SQI derived from the OMDS and TDS exhibited a stronger correlation (r = 0.94, p < 0.01), which was suitable for evaluating the soil quality of the C. dabieshanensis forest. The evaluation results revealed that the soil quality was highest during the early stage of intensive management (IM-3), and the SQI of each soil layer was 0.81 ± 0.13, 0.47 ± 0.11, and 0.38 ± 0.07, respectively. With extended management times, the degree of soil acidification increased, and the nutrient content decreased. Compared with the untreated forest land the soil pH, SOC, and TP decreased by 2.64–6.24%, 29.43–33.04%, and 43.63–47.27%, respectively, following 20 years of management, while the SQI of each soil layer decreased to 0.35 ± 0.09, 0.16 ± 0.02 and 0.12 ± 0.06, respectively. In contrast to extensive management, the soil quality deteriorated more rapidly under longer management and intensive supervision. The OMDS established in this study provides a reference for the assessment of soil quality in C. dabieshanensis forests. In addition, it is suggested that the managers of C. dabieshanensis forests should implement measures such as increasing the amount of P-rich organic fertilizer and restoring vegetation to increase soil nutrient resources for the gradual restoration of soil quality.