The report points out that ontology, data, and software and hardware bases are indeed important supporting elements for the development of embodied intelligence, and they together lay the foundation for the development of embodied intelligence.
Ontology: It is the physical bearing foundation of embodied intelligence.
Determine the ability to interact: Its form and structure determine the way and ability of the agent to interact with the environment. For example, wheeled robots are easy to move quickly on flat ground and are suitable for logistics and transportation scenarios. Quadruped robots have better terrain adaptability and can perform tasks in complex terrain.
Influencing perception and action: Different body designs affect the layout and performance of sensors and actuators. For example, a humanoid robot has a camera and microphone mounted on its head, which can simulate the perception angle of a human. The number of joints and degrees of freedom of a robotic arm determine its flexibility in grasping and manipulating objects.
Data: is the core driver of the development of embodied intelligence.
The key to training a model: to provide a basis for the agent's learning and decision-making. Through a large amount of perception data and corresponding action data, the agent can learn how to respond optimally according to the state of the environment. For example, in an image recognition task, a large amount of annotated image data can help agents accurately identify different objects.
Improve agent performance: Rich and diverse data can enhance the adaptability and generalization ability of agents. By learning data from different scenarios, agents are better able to respond to changes in the real world. For example, the movement data of the robot under different light and temperature conditions can make it operate stably in various environments.
Software and hardware base: It is the technical support for the embodied intelligent operation.
Hardware is the foundation: The performance of computing chips determines the speed of data processing and model computing, such as powerful GPUs and TPUs that accelerate the training and inference of deep learning models. The accuracy and stability of the sensor affect the accuracy of the perception information, the accuracy and force of the actuator determine the execution effect of the action, and the communication module ensures efficient communication between the agent and the external device.
Software is the core driver: the operating system manages hardware resources and provides a stable environment for the operation of agents. Programming languages and development frameworks facilitate developers to implement various functions of agents, and intelligent algorithm libraries are the key to realizing intelligent behaviors, such as reinforcement learning algorithms, which allow agents to learn optimal strategies through continuous trial and error.
Figure: Ontology, data, and hardware and software bases are indeed important supporting elements for the development of embodied intelligence
Security & Privacy – Ensure that embodied intelligence is executed securely and trusted
Security and privacy assurance capabilities are the key to the successful application and promotion of embodied intelligence.
Safety and security
Protection against malicious attacks: Embodied intelligence systems are often connected to networks, which makes them vulnerable to cyberattacks. Hackers may try to take control of an agent to perform harmful actions or steal sensitive information. By implementing security measures such as encrypted communication, authentication, and access control, unauthorized access and malicious attacks can be effectively prevented.
Ensure system stability: Security vulnerabilities can cause systems to crash or behave abnormally, which not only affects the proper functioning of embodied intelligence, but can also cause harm to the surrounding environment and people. For example, a safety breach in a self-driving car can lead to a traffic accident. Through regular security testing and vulnerability fixing, the stability and reliability of the system can be ensured.
Securing users and the environment: Embodied intelligence may physically interact with the user or environment while performing tasks. For example, when a robot is caring for an elderly person or child, it must ensure that its movements are safe and do not cause harm to the user. With secure design and real-time monitoring, it is possible to ensure that the behavior of the embodied intelligence meets safety standards.
Privacy Guaranteed
Protecting user data: Embodied intelligence collects and processes large amounts of user data during its operation, which may contain personal privacy information. For example, smart home devices may collect data on the user's daily activity habits, health, and more. Through encrypted storage and anonymization, it is possible to ensure that user data is not leaked or misused.
Compliance with laws and regulations: With increasingly stringent data protection regulations, such as the European Union's General Data Protection Regulation (GDPR) and China's Personal Information Protection Law, businesses have a responsibility to ensure the lawful use and protection of user data. Privacy safeguards can help businesses comply with these regulations and avoid legal risks.
Enhance user trust: User trust in embodied intelligence is the foundation of its widespread application. Users may be wary of using these technologies if they are concerned that their privacy will be violated. Through transparent privacy policies and strong privacy protection measures, users' trust in embodied intelligence can be enhanced, promoting its wider application.
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Analysis Development of Embodied Intelligence (2)
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Analysis Development of Embodied Intelligence (5)
Analysis Development of Embodied Intelligence (6)
