The development of embodied intelligence is not only a technical challenge, but also a practical challenge. At the product level, its form design and internal hardware architecture directly affect the limits of its ability to act. In order to make embodied intelligence successfully implemented in the real world, robot products need to have reasonable configuration, high compatibility, rich interfaces, good motion performance and high reliability.
First of all, the core challenge is to create a universal and high-performance embodied body. In the process of product development, key factors such as the computing power and cost of computing chips, the transmission efficiency of communication buses, and energy consumption need to be considered at the same time. For example, in long-running environments, battery life is critical; In scenarios that require high real-time performance and stability, the efficiency of cloud communication and the inference capability of the ontology-side chip are particularly critical. In addition, the task scenario of performing delicate operations requires the robot to have stronger flexibility and adaptability, and in complex environments such as the field, the body needs to have excellent impact and drop resistance. Achieving these goals requires not only a deep understanding of application requirements, but also the optimal balance between execution reliability, task efficiency, and cost control.
Secondly, the deep integration of software and hardware systems is also a major challenge. Although the basic model of embodied intelligence is constantly improving in terms of multimodal processing and generalization capabilities, its action ability is still highly dependent on complex functional control algorithms. The motion control algorithm is closely connected to the hardware architecture, and the system design inside the product directly determines the upper limit of the action capability. For example, Boston Dynamics' Spot quadruped robot can stably walk in complex terrain with advanced motion control algorithms, but it‘s hardware architecture limits its ability to perform fine operations and human-computer interaction, making it more suitable for outdoor inspections and lacking in flexible use of tools.
Figure: Schematic diagram of the embodied intelligent industry chain
At the commercialization level, the clarity of market demand and user acceptance will determine the speed of the promotion of embodied intelligence. Although the technology is promising, the application scenarios and business models are not yet fully clear, and there are many challenges.
First of all, the issue of scene selection and openness. The service industry, manufacturing industry, and consumer markets are all likely to be important application areas for embodied intelligence. However, the current large-scale commercialization still needs to give priority to scenarios with high fault tolerance rate and strong user willingness to pay, which makes the identification and prediction of market demand a key problem for commercial implementation.
Second, user acceptance and trust building are crucial. Consumers' recognition and trust in embodied intelligence technology need to be continuously cultivated, which plays a decisive role in the commercialization of technology. For example, in the medical industry, despite the progress of robotic-assisted surgical technology, user acceptance of its safety and reliability is still increasing, which has affected its large-scale adoption to some extent.
Finally, security and privacy risks cannot be ignored. In terms of data privacy, robots collect users' voice commands, biometrics and other information through sensors such as cameras and microphones, which may cause data security issues. At the level of physical safety, due to the strong movement ability of robots, in the event of an accident, it may cause injury to the surrounding environment or personnel. In addition, when it comes to system security, hackers can pose a potential threat to users by tampering with instructions, remotely manipulating devices, or stealing sensitive information.
In addition, embodied intelligence also faces challenges at the industry chain level, including technology, data, market, and policy. Technically, it is necessary to solve the problems of limited perception and comprehension ability, insufficient decision-making and planning ability, and motor control and coordination. In terms of data, the scarcity of high-quality embodied data and the high cost of data collection and annotation are the main obstacles. In the market, the development of the embodied intelligence market in different regions is unbalanced, and it is necessary to deal with the problems of hardware performance, algorithm optimization and cross-industry cooperation. In addition, computing power and energy constraints need to be overcome, and data security and privacy protection must be ensured. Policy support and the development of industry standards are also crucial to drive the development of this emerging field.
In general, the commercialization of embodied intelligence requires not only technological breakthroughs, but also continuous optimization in product design, market application, and safety management to promote its wider application.
Related:
Analysis Development of Embodied Intelligence (1)
Analysis Development of Embodied Intelligence (2)
Analysis Development of Embodied Intelligence (3)
Analysis Development of Embodied Intelligence (4)
Analysis Development of Embodied Intelligence (5)
Analysis Development of Embodied Intelligence (6)
Analysis Development of Embodied Intelligence (7)
