Soft robotics is closely related to embodied intelligence in the joint exploration of the means to achieve more natural and effective robotic behaviors via physical forms and intelligent interactions. Embodied intelligence emphasizes that intelligence is affected by the synergy of the brain, body, and environment, focusing on the interaction between agents and the environment. Under this framework, the design and control strategies of soft robotics depend on their physical forms and material properties, as well as algorithms and data processing, which enable them to interact with the environment in a natural and adaptable manner. At present, embodied intelligence has comprehensively integrated related research results on the evolution, learning, perception, decision making in the field of intelligent algorithms, as well as on the behaviors and controls in the field of robotics. From this perspective, the relevant branches of the embodied intelligence in the context of soft robotics were studied, covering the computation of embodied morphology; the evolution of embodied AI; and the perception, control, and decision making of soft robotics. Moreover, on this basis, important research progress was summarized, and related scientific problems were discussed. This study can provide a reference for the research of embodied intelligence in the context of soft robotics.

In order to increase the compatibility between underwater robots and the underwater environment and inspired by the coconut octopus’s underwater bipedal walking, a method was proposed for bipedal walking for an underwater soft robot based on a spring-loaded inverted pendulum (SLIP) model. Using the characteristics of octopus tentacles rolling on the ground, a wrist arm was designed using the cable-driven method, and an underwater SLIP bipedal walking model was established, which makes an underwater soft robot more suitable for moving on uneven ground. An underwater bipedal walking soft robot based on coconut octopus was then designed, and a machine vision algorithm was used to extract the motion information for analysis. Experimental analysis shows that the underwater bipedal walking robot can achieve an average speed of 6.48 cm s⁻¹, and the maximum instantaneous speed can reach 8.14 cm s⁻¹.

The cable-driven soft arm is mostly made of soft material; it is difficult to control because of the material characteristics, so the traditional robot arm modeling and control methods cannot be directly applied to the soft robot arm. In this paper, we combine the data-driven modeling method with the reinforcement learning control method to realize the position control task of robotic soft arm, the method of control strategy based on deep Q learning. In order to solve slow convergence and unstable effect in the process of simulation and migration when deep reinforcement learning is applied to the actual robot control task, a control strategy learning method is designed, which is based on the experimental data, to establish a simulation environment for control strategy training, and then applied to the real environment. Finally, it is proved by experiment that the method can effectively complete the control of the soft robot arm, which has better robustness than the traditional method.

The emergence of photovoltaic/thermal (PV/T) technology has effectively solved the problem of high temperature and low electrical efficiency of photovoltaic cells, and significantly improved the utilization rate of solar energy. At present, improving the thermoelectric performance of PV/T systems is a research hotspot. The effects of operating parameters such as inlet temperature, solar radiation, ambient temperature, and coolant mass flow rate, are investigated through numerical simulations. An experimental platform is built to verify the effectiveness of the three-dimensional numerical model. It is found that when the solar radiation changes from 800 W/m² to 1000 W/m², the increase rate in thermal efficiency will obviously slow down. When the coolant mass flow rate is increased from 60 to 320 L/h, the thermal efficiency is raised by 8.24%. For each 40 L/h increase in mass flow rate, the electrical efficiency increased by 0.047%. However, when the mass flow rate is too large, the increase in electrical and thermal efficiency gradually decreases. Orthogonal experiments and analysis of variance (ANOVA) are used to study the effects of each parameter and parameter combination on overall efficiency. The results show that ambient temperature has the greatest effect, followed by inlet temperature. Finally, the mathematical model of overall efficiency is established, and the coolant mass flow control formula is proposed. This formula can determine the optimal flow rate according to different environmental conditions, so that the system can operate under the optimal flow rate at all times and maximize the thermoelectric efficiency. Experimental results show that flow control increases the overall energy gain by 2.5% compared with the optimal constant mass flow.

In developing and exploring extreme and harsh underwater environments, underwater robots can effectively replace humans to complete tasks. To meet the requirements of underwater flexible motion and comprehensive subsea operation, a novel octopus-inspired robot with eight soft limbs was designed and developed. This robot possesses the capabilities of underwater bipedal walking, multi-arm swimming, and grasping objects. To closely interact with the underwater seabed environment and minimize disturbance, the robot employs a cable-driven flexible arm for its walking in underwater floor through a bipedal walking mode. The multi-arm swimming offers a means of three-dimensional spatial movement, allowing the robot to swiftly explore and navigate over large areas, thereby enhancing its flexibility. Furthermore, the robot’s walking arm enables it to grasp and transport objects underwater, thereby enhancing its practicality in underwater environments. A simplified motion models and gait generation strategies were proposed for two modes of robot locomotion: swimming and walking, inspired by the movement characteristics of octopus-inspired multi-arm swimming and bipedal walking. Through experimental verification, the robot’s average speed of underwater bipedal walking reaches 7.26 cm/s, while the horizontal movement speed for multi-arm swimming is 8.6 cm/s.