Adaptive soft-matter machines are developed using the complex multiphase organohydrogels with multistable mechanical states.

With the advance of smart material science, robotics is evolving from rigid robots to soft robots. Compared to rigid robots, soft robots can safely interact with the environment, easily navigate in unstructured fields, and be minimized to operate in narrow spaces, owning to the new actuation and sensing technologies developed by the smart materials. In the review, different actuation and sensing technologies based on different smart materials are analyzed and summarized. According to the driving or feedback signals, actuators are categorized into electrically responsive actuators, thermally responsive actuators, magnetically responsive actuators, and photoresponsive actuators; sensors are categorized into resistive sensors, capacitive sensors, magnetic sensors, and optical waveguide sensors. After introducing the principle and several robotic prototypes of some typical materials in each category of the actuators and sensors. The advantages and disadvantages of the actuators and sensors are compared based on the categories, and their potential applications in robotics are also presented.

Soft grippers based on fluidic elastomer actuators have the characteristics of gentle and adaptable grasping that is difficult to realize by rigid grippers. However, it remains challenging to implement a compact gripping device that has multiple bending configurations to exert appropriate force, and sensory capabilities to evaluate the grasping state. Here, we present a soft gripper with variable effective lengths (VELs) that is achieved by rapidly softening selective shape memory polymer sections (within 0.6 s) via a flexible heater. A vortex tube is used to jet cold airflow to accelerate the stiffening process (within 14 s). We show that the soft gripper can not only identify objects but also exert higher gripping force by setting appropriate length according to pneumatic-thermal hybrid actuation. We further propose a touch-reconfiguration-grasp strategy to showcase the synergy of VELs and sensory feedback. The gripper first touches the object under the fully softened state and evaluates the grasping condition based on the sensors’ feedback, then reconfigures the bending length and grasps the object until successful. We envision that soft grippers with sensing ability and reconfigurable grasping configurations would be promising for future applications in unconstructed environments.

Soft wearable devices for hand rehabilitation can assist activities of daily living. While existing soft robotic gloves can help with finger flexion, they are less suitable for patients who cannot extend their fingers due to poststroke increased muscular tension. In this letter, we proposed a variable stiffness pneumatic actuator that effectively increases the elasticity of the actuator to facilitate finger extension. The actuator consists of a fiber-reinforced actuator and a multi-stage articulated elastomer. It bends and extends by pneumatic actuation, with multi-stage articulated elastomer providing stiffness adjustment to accommodate varying joint resistance. The variable stiffness mechanism was achieved by the multi layered discrete metal plates which can gracefully fit the unique structure of human fingers. Analysis models were established to quantify the actuator's air pressure, vacuum pressure, and bending angle, also to predict the actuator's stiffness under different vacuum pressures. Experiments and finite element simulation were carried out to evaluate the bending performance of the actuator. Meanwhile, the output force and variable stiffness characteristic of the actuator were tested. The results show that the actuator can achieve 1.54 N fingertip output force and 2.4 times stiffness change, which satisfy the requirement of stroke patient. In addition, a clenched artificial hand with similar damper forces to human hand was applied to further verify the extending effect of the variable stiffness actuator. Finally, a rehabilitation glove integrated with multiple actuators was tested to assess typical rehabilitation gestures and the ability to grasp daily objects.

Hitherto, automated grasping with robotic grippers requires adjusting the posture and force of the fingers based on the size, geometry, stiffness, and pose of the objects. To provide a simpler but efficient grasping methodology, a soft enveloping gripper is presented and investigated how its morphological adaptability improves the grasping ability by comparing its performance with fingered grippers. Results show that this enveloping gripper can omnidirectionally envelop objects via active–passive interaction, which allows the gripper to 100% grasp the object located at different positions within range and keep their orientations. However, the grasping success rate and orientation error of the fingered grippers highly depend on the relative position and angle of the objects to the grippers, as well as the number of fingers. The dynamic vibration and decay time of the enveloping gripper when grasping a 500 g weight are, both, approximately one‐sixth of those of the two‐fingered gripper when grasping a 12.37 g cube. This enveloping gripper can automatically grasp objects (including deformable ones) lying in different poses without posture estimation and force control with a simple vision‐based automatic grasping method. The enveloping grasping method may open an avenue for simple, low cost yet powerful automatic grasping applications.

Multirotor drones are widely used in fields such as environmental monitoring, agricultural inspection, and package delivery, but they still face numerous challenges in durability and aerial operation capabilities. To address these issues, this paper presents a biomimetic leg-claw mechanism (LCM) inspired by the biomechanics of birds. The claw of the LCM adopts a bistable gripper design that can rapidly close through external impact or actively close via the coordination of internal mechanisms. Additionally, its foldable, parallelogram-shaped legs bend under external forces, stretching the main tendon. A ratchet and pawl mechanism at the knee joint locks the leg in the bent position, thereby enhancing the gripping force of the claw. This paper calculates and experimentally verifies the degrees of freedom in different states, the forces required to open and close the gripper, the application scenarios of active and passive grasping, and the maximum load capacity of the mechanism. Furthermore, perching experiments demonstrate that the LCM enables the drone to perch stably on objects of varying diameters.