The integration of artificial intelligence with robotics is catalyzing the emergence of a new generation of robotic forms, where embodied intelligence robots stand out due to their core characteristics of physical embodiment and environmental interaction. This article systematically reviews the conceptual evolution and current development of embodied intelligence robots, focusing on the transformative impact of AI technologies across perception, cognition, decision-making, execution, and data support. By examining core technologies such as multimodal perception, large language models, and deep reinforcement learning, and illustrating their applications in industrial manufacturing, healthcare, and domestic services, this article showcases the achievements of AI-empowered embodied intelligence robots. Furthermore, it addresses practical bottlenecks like computational resource consumption and insufficient algorithmic generalization and robustness, while envisioning future trends such as more efficient model architectures, cross-modal collaboration, and multi-domain expansion. This provides a reference for technological innovation and industrial implementation of embodied intelligence robots.
Modern AI originated in the 1950s with the Dartmouth Conference, evolving from symbolicism to connectionism based on neural network models. Embodied intelligence emerged as a reflection on the limitations of traditional symbolicism and connectionism, emphasizing that intelligence arises from the interaction between body, environment, and cognition. In the 1980s, scholars proposed that intelligence requires embodiment and situationalization, promoting the development of bionic robots and reinforcing the notion that action is cognition. With interdisciplinary integration of materials, control, and learning, embodied intelligence has developed into a key research paradigm distinct from disembodied intelligence, focusing on interaction and morphology, and becoming a critical direction for breakthroughs in next-generation AI. Contemporary embodied intelligence refers to intelligent systems that integrate multimodal perception, autonomous learning, behavioral decision-making, and human-robot collaboration, emphasizing the ability of agents to exhibit high adaptability and evolutionary capabilities in dynamic, uncertain environments through continuous interaction between the body and environment. Core features include environmental perception and cognitive integration, cross-task autonomous adaptation and optimization, and synergy and practicality in scenarios such as services and manufacturing.
An embodied intelligence AI robot refers to a robot with a physical entity capable of interacting with the real environment through its own perception, decision-making, and action abilities. Compared to traditional robots, embodied intelligence AI robots not only possess mechanical structures and movement capabilities but, more importantly, have intelligent perception and decision-making abilities similar to humans, allowing them to autonomously adjust action strategies based on environmental changes. Their embodied characteristics enable them to directly contact and influence the surrounding environment, fully understand the physical features and semantic information of the real world, complete tasks through real-time interaction between the body and environment, and achieve continuous learning of intelligence based on physical world interaction feedback. For example, traditional industrial robotic arms can only perform preset tasks like welding or搬运, while embodied intelligence AI robots, such as Optimus, can understand complex scenes through multimodal perception and autonomously plan paths, grasp objects, and even collaborate with humans on dynamic tasks.
As a strategic direction for promoting industrial intelligent upgrading, AI+ combined with robotics has given rise to diverse forms of intelligent robots, which vary significantly based on their intelligence level, interaction depth, and application scenarios. Among them, embodied intelligence AI robots represent an advanced form of AI+ robotics, emphasizing that the agent must have a physical entity to perceive, interact, and act in the real physical environment, and continuously learn and optimize based on environmental feedback. As shown in Table 1, compared to generalized AI+ robotics, embodied intelligence AI robots exhibit three characteristics: necessity of physical entity, depth of environmental interaction, and continuity of closed-loop learning. Therefore, embodied intelligence AI robots are an important manifestation and frontier direction of AI+ empowered robotics. The development of embodied intelligence AI robots has progressed from early behavioral control exploration to highly complex systems integrating perception, control, learning, and cognition, undergoing four stages: theoretical foundation, engineering implementation, productization attempts, and system integration upgrade, now becoming a key technological breakthrough in next-generation AI.
| Feature | Traditional Robot | AI+ Robot (Generalized) | Embodied Intelligence AI Robot (Subset of AI+ Robot) |
|---|---|---|---|
| Core Drive | Program Control | AI Algorithm Driven | AI Algorithm Driven |
| Intelligence Level | Low (Execute Preset Tasks) | Medium to High (Have Perception, Decision-Making Abilities) | High (Emphasize “Perception-Cognition-Decision-Execution” Closed Loop) |
| Interaction Depth | Shallow (Limited Environmental Interaction) | Diverse (Depending on Specific Application) | Deep (Active Interaction Through Physical Entity, Feedback Learning) |
| Environmental Adaptability | Low (Dependent on Structured Environment) | Medium to High (Depending on AI Capabilities) | High (Need to Adapt to Unstructured, Dynamic Environments) |
| Learning Ability | None or Weak | Yes (Based on Data/Models) | Strong (Emphasize Continuous Learning Based on Environmental Interaction) |
| Typical Examples | Industrial Robotic Arm (Basic Functions) | Smart Sweeping Robot, Smart Customer Service Robot | Humanoid Robot, Advanced Nursing Robot |
In recent years, embodied intelligence AI robots have made significant progress in technology and applications. Technologically, robot hardware performance has continuously improved, with higher sensor accuracy, richer varieties, and more efficient and precise motor drive systems, providing a solid foundation for perception and movement of embodied intelligence AI robots. In software algorithms, AI technologies, especially the introduction of multimodal large model capabilities, have greatly enhanced the intelligence level of embodied intelligence AI robots. From early simple programming control, it has evolved to the use of machine learning and deep learning algorithms to achieve autonomous perception, interaction, decision-making, and optimized learning. In application fields, compared to traditional mobile robots, embodied intelligence AI robots can complete complex tasks that typically require human intelligence, thus being widely applicable in industrial manufacturing, logistics and warehousing, healthcare, and domestic services. In industrial manufacturing, embodied intelligence AI robots can perform complex, flexible assembly tasks, improving production efficiency and quality; in logistics and warehousing, they enable intelligent搬运 and sorting of goods; in healthcare, they assist medical staff in rehabilitation therapy and other tasks. In 2009, Boston Dynamics launched the quadruped robot BigDog and the humanoid robot Petman, and developed the first-generation Atlas prototype in 2013, marking the entry of embodied intelligence AI robots into the engineering breakthrough stage. In 2013, companies such as Hanson Robotics and Ubtech entered the field, promoting the industrialization of embodied intelligence AI robots. In 2016, Hanson Robotics launched the social robot Sophia, and in 2018, Ubtech released the bipedal service robot Walker, with embodied intelligence AI robots gradually possessing comprehensive capabilities such as human-robot interaction, multimodal perception, and autonomous navigation, entering the commercialization attempt stage. In 2023, Tesla launched the Optimus series of humanoid robots, integrating human-like operation, environmental understanding, and energy optimization design, pushing embodied intelligence into a new era of system integration upgrade.
Overall, embodied intelligence AI robots have evolved from single behavior control to complex systems integrating perception, cognition, and decision control, becoming an important direction for technological breakthroughs and industrial implementation of next-generation AI. Currently, most applications of embodied intelligence AI robots domestically and internationally are still in the laboratory testing stage. Although embodied intelligence AI robots for specific scenarios and tasks have developed significantly, overall technology is not yet mature, and industrialization and commercialization have not been achieved.
The application of AI+ in the robotics field covers a wide range of technologies, from traditional image recognition to the latest multimodal large model methods. When empowering the specific form of embodied intelligence AI robots, the technological system exhibits deep integration characteristics around the closed loop of perception-cognition-decision-execution-data. Specifically, the key technological system of embodied intelligence AI robots includes: multimodal perception and understanding technology, which builds environmental semantic representations through the fusion of multi-source information such as vision, language, and touch; multimodal planning and decision technology, which converts high-level semantics into executable action sequences in the physical environment based on large language models and world models; motion control technology, which combines model predictive control and deep reinforcement learning to achieve high-precision, adaptive execution; and multimodal generative AI technology, which uses synthetic data to drive model iterative optimization.
These technologies together form a complete closed loop from perception to execution for embodied intelligence AI robots. This article systematically elaborates on the key AI technologies supporting the development of embodied intelligence AI robots, including not only the currently significantly breakthrough large model methods but also innovative applications of other foundational AI methods in robot perception, planning, control, and other links. The key technological system of AI+ empowered embodied intelligence AI robots can be summarized as four progressively core links: first, multimodal perception and understanding provide a unified environmental representation across information such as vision, language, and touch for embodied intelligence AI robots; second, multimodal planning and decision-making use large language models and world models to convert high-level semantics into executable action sequences in the physical environment; third, motion control achieves high-precision, adaptive execution under real physical constraints through the synergy of model prediction and reinforcement learning; fourth, multimodal generative AI continuously drives the iterative optimization of the aforementioned links with low-cost, high-fidelity synthetic data. Figure 1 visually shows the core AI technology links and typical methods supporting the development of embodied intelligence AI robots.
In embodied intelligence AI robot systems, multimodal perception technology is the foundational capability for intelligent agents to understand complex environments, make behavioral decisions, and interactively control. With the development of large-scale multimodal models, the perception paradigm of embodied intelligence has evolved from traditional single-modal input and rule-driven to a deep understanding mechanism centered on the fusion of multimodal information such as language, vision, speech, and touch. Currently, research on perception and understanding capabilities of embodied intelligence AI robots mainly revolves around two paths: one is the use of multimodal models for environmental perception and task understanding; the other is multimodal modeling for environmental representation and semantic enhancement, forming a relatively systematic technological system.
Multimodal large models possess deep understanding capabilities for various information forms such as images, text, charts, and documents, covering multilingual and multimodal semantic parsing, which can provide environmental perception and semantic understanding support for embodied intelligence, and generate structured outputs such as task decomposition and control instructions through prompting mechanisms, thereby enabling intelligent agents to adapt to and operate in complex environments. An enhanced embodied task planning framework based on GPT-4V shows that embodied intelligence systems can achieve high-consistency behavior generation by combining image frames and language instructions, demonstrating the application potential of pre-trained multimodal models in the perception-cognition-planning chain. Further, the ViLA system achieves closed-loop control based on GPT-4V, guiding behavior adjustment through dynamic visual feedback, significantly improving the robustness and adaptability of AI robots in dynamic environments. Another object-centric embodied large language model defines action tokens and state tokens, enabling the language model to complete action generation and state perception cycles under the guidance of multimodal feedback, providing a new paradigm for natural language-driven embodied interaction.
In environmental modeling and spatial understanding, multimodal models are used to build semantically enhanced scene representations. Pre-trained vision-language models such as CLIP are widely used in open-vocabulary object recognition, image semantic embedding, and scene semantic understanding. For example, the HomeRobot system introduces CLIP for weakly supervised 3D semantic modeling, enabling AI robots to recognize and manipulate in open environments. A voxelized scene-based algorithm can construct attention amplification paths from coarse to fine through attention mechanisms, improving regional focus ability in perception and control; while a 3D feature field model encodes multimodal features into 3D grids and feature fields, further enhancing the generalization ability of AI robots in visual positioning and semantic retrieval.
Notably, in recent years, 3D Gaussian-based scene modeling methods have shown extremely high efficiency and accuracy in multimodal perception. Combining 3D Gaussians with language features can build semantic fields responsive to natural language queries, achieving efficient rendering and interactive queries, and can further introduce Gaussian representations into embodied tasks, building embodied interaction systems integrating semantic understanding, real-time editing, and grasp generation.
In summary, the multimodal perception system of modern embodied intelligence AI robots is transitioning from perceptual fusion to comprehensive leaps in semantic modeling, feedback regulation, and task adaptation. The language-vision embedded structure centered on large models, voxelized and Gaussianized scene modeling, and semantically enhanced behavioral closed-loop control constitute the key technological paths of embodied multimodal perception. These advances significantly improve the generalization ability, operation accuracy, and environmental adaptability of embodied intelligence AI robots, and also provide a solid technical foundation for the construction of future multimodal intelligent agents.
In the technological system of embodied intelligence AI robots, environmental modeling and positioning are core modules for constructing their spatial cognition, directly determining whether AI robots can achieve autonomous navigation, task execution, and safe interaction in dynamic environments. The essence of this technology is to solve two core problems: where am I (positioning) and what is around (environmental modeling). The output spatiotemporal data is not only the end point of the perception layer but also the starting point of the decision layer, providing basic semantic support for motion planning and task reasoning.
Traditional simultaneous localization and mapping technology constructs environmental maps in real-time through sensor data and determines own coordinates, serving as the eyes of embodied intelligence AI robots for autonomous exploration in unknown environments. In indoor service scenarios, laser SLAM (e.g., Cartographer algorithm) uses 360° lidar to build point cloud maps with centimeter-level accuracy, planning collision-free paths for sweeping AI robots; while visual SLAM (e.g., ORB-SLAM) extracts environmental features through monocular/binocular cameras, widely used in consumer-grade AI robots due to lightweight advantages. As embodied intelligence extends to complex scenarios, semantic SLAM becomes a key breakthrough direction, no longer limited to geometric structure modeling but endowing maps with semantic labels such as doors, stairs, and dining tables through deep learning (e.g., Mask R-CNN semantic segmentation), enabling AI robots not only to see obstacles but also to understand object functions. For example, when an AI robot receives the instruction go to the kitchen to take out the trash, the semantic map can directly locate the position and opening state of the kitchen door, combined with geometric maps to plan obstacle avoidance paths, greatly improving task execution efficiency.
The advancement of environmental modeling and positioning technology is promoting embodied intelligence from geometric-level navigation to cognitive-level interaction. When the map of an AI robot not only contains coordinate points and obstacles but also integrates prior knowledge such as object functions (e.g., refrigerators for storing food, sockets to avoid touching) and spatial relationships (e.g., cups usually on dining tables), its decision logic will upgrade from simple obstacle avoidance to understanding environmental intent. For example, an AI robot equipped with semantic dynamic SLAM, when entering an unfamiliar room, can autonomously plan action routes conforming to human habits by recognizing semantic information such as desk → may have computer and trash can → should avoid proximity. This intelligence based on spatial cognition is the core premise for embodied intelligence AI robots to achieve complex tasks.
Environmental modeling and positioning technology, like the spatial memory system of AI robots, directly determines the adaptability of AI robots in unstructured environments through its accuracy and intelligence level. With breakthroughs in technologies such as multi-sensor fusion (e.g., deep integration of laser-visual-inertial data) and lightweight models (e.g., real-time 3D reconstruction based on NeRF), this core module is gradually endowing embodied intelligence AI robots with spatial understanding abilities close to humans—not only seeing the physical world but also reading environmental semantics, laying a solid foundation for the perception-decision closed loop.
The multimodal planning and decision layer, as the intelligent brain of embodied intelligence AI robots, is responsible for reasoning future action sequences based on perceptual information and outputting trajectories, postures, or collaborative schemes that meet task constraints.
Multimodal large models integrate multi-source perception such as vision, language, depth, and touch through hierarchical collaborative architectures, reasoning future action sequences in dynamic environments and outputting trajectories, postures, or collaborative schemes that meet physical constraints and task objectives. Based on the results of physical perception and semantic parsing, the intelligent brain performs task decomposition and long-term planning: large language models encode human instructions, for example, decomposing water the plant into subtasks such as locate watering can, grasp, move, and water, combined with multimodal SLAM to build environmental maps with both geometric accuracy and semantic information, while continuously accumulating interaction experience through embodied memory to support task interruption recovery and value alignment.
Recent research focuses on three representative directions: first, zero-shot operation planning, where language models call vision-language models to synthesize 3D value maps, then use greedy search to generate collision-free end poses, achieving zero-shot completion of hundreds of daily operations; second, 3D world model-driven, where visual-language-action models based on 3D physical information embed scene, object, and action features into a unified 3D Transformer framework, given initial and target states, can imagine depth maps and point clouds after task completion and output action sequences, another interactive video generation model marks vision, action, and reward as autoregressive sequences, serving as conditional video prediction and providing scalable world models for reinforcement learning; third, multi-robot collaborative planning, a multi-robot collaboration method uses pre-trained large language models for high-level communication and low-level path planning, where agents discuss task strategies in natural language and generate subtask plans and task space waypoint paths.
Overall, multimodal planning and decision technology is rapidly evolving along three main lines: large models combined with 3D value mapping to achieve interpretable zero-shot operation, world models combined with temporal imagination to support prediction-control integration, and multimodal large models driving natural language collaboration to simplify navigation and multi-robot division of labor, enabling embodied intelligence AI robots to obtain efficient, robust, and generalizable decision-making abilities with minimal prior knowledge and annotation.
Motion control algorithms, as the motor cerebellum of embodied intelligence AI robots, are responsible for mapping high-level decisions into executable joint instructions. Their technological route has gone through three stages: rule-based, model-based, and learning-based, and is gradually transitioning to multi-algorithm fusion. Rule-based control algorithms include zero moment point dynamic balance, proportional-integral-derivative control, system协同 control, and disturbance compensation, with advantages of simple implementation and high real-time performance, but limited adaptability and scalability. Model-based control algorithms, represented by model predictive control and whole-body control, combine precision and physical interpretability but rely heavily on accurate dynamic models and computing power, with long development cycles. Learning-based control algorithms, i.e., deep reinforcement learning and imitative learning, can automatically explore optimal strategies in unknown environments, significantly reducing controller parameter tuning costs, but require training samples and high-fidelity simulation platforms.
In current industrial applications, the control strategies of traditional industrial machines are still dominated by traditional model control schemes controlled by visual-temporal networks. Taking electronic component welding as an example, through CNN recognition of micron-level solder joint positions, then using RNN to predict the time series of robotic arm motion trajectories, achieving control accuracy of ±0.1 mm, meeting the requirements of high-precision device assembly; after introduction in a semiconductor packaging production line, welding yield increased from 85% to 99.2%, and production efficiency improved by 40%.
At the laboratory level, cerebellar motion control is moving from rule-driven to model-learning fusion. Learning mechanisms endow AI robots with self-adaptability in complex dynamic environments, while MPC and WBC remain key for safety constraints and force control. In the future, the improvement of high-fidelity simulation platforms and high-quality datasets will further promote multi-algorithm collaboration, driving embodied intelligence AI robots to achieve higher levels of precise, flexible, and reliable motion control.
Data is key to training and optimizing embodied intelligence systems. Multimodal generative AI technology, as an important part of the AI+ toolbox, is increasingly becoming an important support means for training embodied intelligence AI robots, especially when facing common industry problems such as high real data acquisition costs, difficult annotation, and high scene complexity. Its large-scale, low-cost 2D/3D data synthesis ability shows great potential in the training links of embodied intelligence models combined with simulation and deep learning.
Deep learning-based large model generation technology is reshaping multimodal content generation methods, mainly relying on mainstream technological paths such as diffusion models and Transformer architectures. Diffusion models synthesize high-fidelity images by modeling the process of noise addition and gradual denoising, with typical representatives such as Stable Diffusion, not only achieving image quality close to the real world but also optimizing computational efficiency and generation speed. The Imagen model introduces large-scale pre-trained language models on this basis, significantly improving semantic alignment in text-to-image generation, supporting more detailed natural language-driven image generation. The Transformer architecture, based on self-attention mechanisms, can effectively model long-distance dependencies, showing excellent capabilities in image-text joint modeling, cross-modal understanding, and generation. Representative achievements such as OpenAI’s DALL·E series, trained on large-scale image-text paired data, enable models to generate consistent images for complex semantic descriptions; while DeepMind’s Gato model goes further, proposing a unified input/output paradigm, achieving multi-task, multi-modal integration of perception, operation, and language tasks.
From the perspective of technological development trends, diffusion models focus on generation quality and detail control, while Transformer architectures have more advantages in multimodal understanding and language guidance. The combination of the two is promoting generative models towards stronger generalization abilities and more flexible command response. NVIDIA’s Cosmos World Foundation Model is an application example of learning-based large model generation technology, which constructs physically consistent, high-fidelity, multimodal fused training data and simulation environments through large model generation capabilities, thereby improving the perception, prediction, and decision-making abilities of embodied intelligence AI robots.
Physics-based large model generation methods introduce physical information into the generation framework, combining generative adversarial networks and variational autoencoders to build virtual simulation spaces highly consistent with the real physical environment. This model adopts a generate-discriminate-optimize closed-loop architecture, where the generator builds diverse virtual scene data, the discriminator performs authenticity and rationality discrimination, and iterative optimization of generation ability through adversarial learning. Technically, this model supports controllable scene generation functions such as multi-view switching and lighting adjustment, and can be used to build high-fidelity training samples covering complex industrial environments. Taking industrial assembly scenarios as an example, multimodal large models can generate 3D image data of components under different lighting conditions, posture changes, and occlusion situations, covering over 90% of real working conditions, and forming an efficient data production model of 20% real collection + 80% simulation synthesis; compared to traditional methods, this model can improve training sample acquisition efficiency by more than 10 times, significantly enhancing the adaptation and generalization ability of AI robots to unknown environments.
The key technological system of AI+ empowered embodied intelligence AI robots exhibits closed-loop innovation from data-perception-decision-execution: multimodal large models and semantically enhanced scene reconstruction make the perception link move from single-source collection to high-precision fusion across vision, language, depth, and touch; large language model-driven hierarchical planning architectures use 3D value mapping, world model temporal imagination, and natural language collaboration to enable AI robots to complete zero-shot operation, end-to-end navigation, and multi-machine collaboration with minimal prior knowledge; hybrid control algorithms combining model predictive control and deep reinforcement learning provide sub-millimeter adaptive performance for fine force control and dynamic obstacle avoidance; multimodal generative large models combining diffusion, Transformer, and physical priors use low-cost synthesis of high-fidelity 2D/3D data to provide continuous evolving training fuel for the above layers. Throughout the overall view, AI technologies form synergistic gains in four dimensions: perception, planning, control, and data generation, accelerating the evolution of AI robot systems towards higher generalization, autonomy, and reliability, laying a solid foundation for large-scale implementation in industrial, service, and exploration scenarios.
In industrial manufacturing, embodied intelligence AI robots will become an important force in improving production efficiency and quality, deeply integrating perception, reasoning, and execution into industrial equipment, accelerating the leap of traditional manufacturing towards intelligence, flexibility, and efficiency. With real-time perception and closed-loop optimization, production systems can not only achieve high automation but also possess self-adaptive and self-iterative abilities, injecting new kinetic energy into the intelligent upgrading of manufacturing. For example, in the automotive manufacturing industry, some car manufacturers have introduced embodied intelligence AI robots equipped with AI technology for the assembly of automotive components. These AI robots can accurately identify the shape and assembly position of components through multimodal perception technology, use reinforcement learning algorithms to continuously optimize assembly actions, and improve assembly accuracy and efficiency. At the same time, with the help of large model technology, AI robots can understand complex assembly process requirements and quickly analyze and solve problems in the production process. When assembling complex components such as engines, embodied intelligence AI robots can accurately grasp various components and assemble them in the specified sequence and torque, greatly improving assembly quality and reducing errors caused by manual operations. Compared with traditional automated production lines, embodied intelligence AI robots can better adapt to the production needs of small batches and multiple varieties, quickly adjust production tasks, and reduce production costs. In the field of flexible production, an intelligent AI robot unit in Changzhou reduces the entire line debugging time from one week to several hours through rapid learning and parameter self-tuning, making multi-variety, small-batch production more flexible and efficient. For operating environments with flammable, explosive, or toxic aerosols, embodied spraying AI robots can use standardized actions to replace manual operations, eliminating safety hazards and ensuring uniform coating quality. The above practices show that embodied intelligence AI robots have become a key driving force for improving quality and efficiency, ensuring safety, and enhancing flexibility in intelligent manufacturing.
In addition, synthetic data based on multimodal generative large model technology is becoming a core resource for training embodied intelligence systems in industries such as robotics and automotive. The current embodied intelligence data technology implementation in the industry is mainly divided into three ideas: first, represented by companies such as Shanghai Zhiyuan Innovation Technology Co., Ltd., mainly relying on real machine data collection, establishing data collection factories in Shanghai, forming a million-level AgibotWorld dataset, and jointly launching a large-scale digital twin platform; second, represented by companies such as Beijing Yinhe General Robot Co., Ltd., purely simulation data-driven, pre-training entirely based on simulation synthetic data, building end-to-end embodied grasping basic large models (e.g., GraspVLA), with training scales reaching billions of vision-language-action pairs; third, represented by companies such as Ubtech, hybrid data training solutions, combining open-source large models and simulation frameworks, using systems such as UNDERS2 to generate diverse training scenes locally at low cost, achieving integration of real machine data and synthetic data.
Currently, the mainstream embodied intelligence industry is forming a hybrid data training paradigm dominated by the third data solution, with simulation data-driven as the main and real machine data as auxiliary. Synthetic data based on multimodal generative large model technology is transforming from a supplementary means to the main support for embodied intelligence model training.
In the healthcare field, the application of embodied intelligence AI robots brings better nursing experiences and treatment effects to patients. For example, some rehabilitation therapy AI robots use AI technology to develop personalized rehabilitation training plans based on the patient’s physical condition and rehabilitation needs. Through multimodal perception technology, AI robots can monitor the patient’s movement status, muscle strength, and other physiological data in real-time, and continuously adjust training parameters and action guidance through reinforcement learning algorithms to achieve the best rehabilitation effect. In assisting the daily lives of the elderly or people with mobility impairments, embodied intelligence AI robots also play an important role. These AI robots can understand user instructions through speech recognition and natural language processing technology, complete tasks such as fetching items and assisting walking. With the help of large model technology, AI robots can also conduct simple conversations with users, providing companionship and psychological support. A smart nursing AI robot can remind the elderly to take medication on time and exercise according to their daily habits, while also monitoring the physical condition of the elderly through cameras, and notify medical staff or family members in time if abnormalities are found.
The home service field is an important direction for the application of embodied intelligence AI robots. With the improvement of people’s living standards, the intelligent demand for home services is also growing. Some home service embodied intelligence AI robots use AI technology to complete various tasks such as cleaning, washing dishes, and caring for children. In cleaning tasks, AI robots build home environment maps through sensors such as vision and lidar, use reinforcement learning algorithms to plan optimal cleaning paths, and efficiently complete floor cleaning work. In caring for children, AI robots can interact with children through voice interaction for activities such as games and storytelling, and provide learning assistance to children with the knowledge reserve of large models. When children ask questions, AI robots can quickly find accurate answers through access to large models and provide vivid explanations. The emergence of these home service embodied intelligence AI robots greatly reduces people’s household burden and improves the convenience and intelligence level of family life.
AI+ empowered embodied intelligence AI robots need to process a large amount of perceptual data and run complex algorithm models during operation, which places extremely high demands on computing resources. For example, real-time processing of multimodal perceptual data and reasoning operations of large models require strong computing power support. However, the development of hardware computing resources is still difficult to fully meet the needs of embodied intelligence AI robots, especially in some scenarios with high real-time requirements, computational delays may cause滞后 in AI robot decision-making and action. In addition, high-performance computing is often accompanied by high energy consumption problems, which is a serious challenge for embodied intelligence AI robots that need to operate autonomously for a long time. High energy consumption not only increases usage costs but also limits the endurance and application range of AI robots. Developing efficient and energy-saving computing hardware and optimizing algorithms to reduce computing resource requirements and energy consumption is an important issue facing the current development of embodied intelligence AI robots.
Although AI algorithms can enable embodied intelligence AI robots to perform well in specific scenarios, when facing complex and changing real environments, the generalization ability and robustness of the algorithms still need to be improved. For example, in visual perception algorithms, when environmental lighting, object occlusion, and other situations change, AI robots may experience target recognition errors or loss. Reinforcement learning algorithms during training often rely on specific environmental settings and reward mechanisms. When AI robots enter new environments or tasks change, previously learned behavioral strategies may no longer be applicable. Although large models have powerful knowledge understanding and reasoning abilities, they may also handle some ambiguous or abnormal situations improperly in practical applications. Improving the generalization ability and robustness of algorithms, enabling embodied intelligence AI robots to operate stably and reliably in different environments and tasks, is a key technical problem that needs to be solved urgently.
In the application of embodied intelligence AI robots, the naturalness and safety of human-robot interaction are crucial. Currently, although embodied intelligence AI robots have made certain progress in voice interaction, gesture recognition, etc., there is still a gap with natural and smooth interaction between humans. For example, in voice interaction, the understanding of voice semantics by AI robots is not accurate enough, especially in complex scenarios such as multiple people speaking simultaneously and diverse accents, misunderstandings are prone to occur. In human-robot collaboration tasks, the perception and prediction ability of AI robots for human intentions is insufficient, leading to low collaboration efficiency. In addition, safety is also an important issue in human-robot interaction. In the process of close contact and collaboration between embodied intelligence AI robots and humans, once malfunctions or misoperations occur, it may cause harm to humans. How to design more natural and safe human-robot interaction methods, improve the ability of AI robots to understand human behavior and intentions, and ensure safety during human-robot interaction is a necessary condition for embodied intelligence AI robots to move towards widespread application.
With the continuous development of AI technology, more advanced algorithms and models will be applied to the field of embodied intelligence AI robots in the future. For example, more efficient deep learning architectures may further enhance the perception and cognitive abilities of embodied intelligence AI robots, enabling them to process complex information faster and more accurately. At the same time, the integration between different AI technologies will be deeper, with multimodal perception technology, reinforcement learning, large models, etc., collaborating with each other to form a more powerful intelligent system. For example, by combining reinforcement learning with large models, embodied intelligence AI robots can learn and optimize behavioral strategies more quickly under the guidance of knowledge provided by large models, improving execution ability in complex tasks. In addition, the development of emerging computing technologies such as quantum computing may bring new breakthroughs to the AI application of embodied intelligence AI robots, solving current computing resource bottleneck problems, and promoting the further improvement of the intelligence level of embodied intelligence AI robots.
In the future, embodied intelligence AI robots will develop towards a higher degree of intelligence and autonomy. In terms of perception, AI robots will have more sensitive and comprehensive perception abilities, able to perceive subtle changes in the environment in real-time and deeply understand various complex scenes. In terms of decision-making and action, embodied intelligence AI robots will be able to make more reasonable and efficient decisions autonomously based on perceptual information, flexibly responding to various emergencies and task changes. For example, in rescue scenarios, embodied intelligence AI robots can autonomously judge the danger level of the disaster scene, plan safe and effective rescue paths, and adjust rescue strategies according to actual situations. By continuously improving the level of intelligence and autonomy, embodied intelligence AI robots will be able to replace humans in completing tasks in more complex and dangerous environments, providing stronger support for the development of human society.
With the continuous maturity of AI+ empowered robotics technology, its application fields will further expand and deepen. In the industrial field, embodied intelligence AI robots will not be limited to traditional tasks such as assembly and搬运, but will also play an important role in more complex production links, such as precision processing and quality inspection, promoting the development of industrial manufacturing towards intelligence and flexibility. In the medical field, embodied intelligence AI robots will make greater breakthroughs in surgical assistance, telemedicine, etc., providing patients with more accurate and efficient medical services. In the education field, embodied intelligence AI robots are expected to become an important tool for personalized learning, providing customized teaching content and tutoring based on students’ learning situations and characteristics. In addition, in fields such as agriculture, aerospace, and deep-sea exploration, embodied intelligence AI robots will also exert unique advantages, providing innovative solutions to practical problems in various fields and creating greater social and economic benefits.
AI+ has brought unprecedented opportunities for the development of embodied intelligence AI robots. Through key technologies from multimodal perception and understanding, planning and decision-making, motion control to multimodal generation, a four-layer progressive architecture from top to bottom is formed: perception fusion layer, cognitive planning layer, control execution layer, and data generation layer, ultimately forming a closed-loop system of perception-cognition-control-data mutual coupling, significantly improving the intelligence level and application ability of embodied intelligence AI robots, and achieving impressive application results in multiple fields such as industrial manufacturing, healthcare, and home services. However, the current development of embodied intelligence AI robots still faces many challenges such as computing resources and energy consumption, algorithmic generalization and robustness, and naturalness and safety of human-robot interaction. In the future, with the continuous innovation and integration of AI technology, embodied intelligence AI robots, as an important representative of AI+ robotics, will continuously upgrade towards intelligence and autonomy, and the application fields will further expand and deepen. To promote the continuous development of embodied intelligence AI robot technology, joint efforts from industry, academia, and research are needed to strengthen technology research and development, break through key technology bottlenecks, improve relevant standards and specifications, and ensure that embodied intelligence AI robots better serve human society under the premise of safety and reliability, making greater contributions to economic development and social progress.