In this review, we explore the current state and future directions of embodied robot technology, focusing on its integration of advanced artificial intelligence, mechanical engineering, and materials science. As a pivotal representation of embodied intelligence, embodied robots are poised to revolutionize various sectors by mimicking human-like forms and functionalities. We begin by outlining the definition and historical progression of embodied robots, comparing international and domestic advancements in technology, industry, and policy. Subsequently, we delve into core technological aspects, including critical components, environmental perception, gait control, embodied intelligence, human-robot interaction, and software ecosystems. We also examine typical applications in specialized environments, manufacturing, and services, addressing challenges and projecting future trends. Throughout, we emphasize the role of embodied robots in driving innovation and discuss their potential in emerging fields.
The development of embodied robots can be categorized into distinct phases, each marked by technological leaps. Initially, from 1969 to 2000, research centered on basic mechanical structures and simple motion control, with robots performing repetitive, pre-programmed tasks due to limitations in computing and sensing. Between 2000 and 2015, integration advanced with richer sensors enabling environmental perception, though autonomy remained constrained. From 2015 to 2022, high-dynamic motion capabilities emerged, supported by improved control algorithms and early AI applications, yet real-time decision-making was still evolving. Since 2022, the fusion with large AI models and simulation platforms has accelerated, fostering adaptive learning and broader applicability. This evolution underscores the shift from hardware-centric systems to software-driven embodied intelligence.
| Stage | Period | Key Characteristics |
|---|---|---|
| Early Development | 1969-2000 | Basic mechanical design and motion control; limited to repetitive tasks |
| High Integration | 2000-2015 | Enhanced sensors for perception; partial autonomy in decision-making |
| High Dynamic Motion and Intelligence | 2015-2022 | Advanced control for complex motions; initial use of深度学习 and reinforcement learning |
| Rapid Development | 2022-present | Integration with large models and embodied intelligence; real-time adaptation |
Globally, embodied robot technology has seen significant progress, with key contributions from regions like the United States, Japan, and Europe. International efforts focus on electric-driven designs for flexibility and safety, coupled with AI integrations that enhance cognitive abilities. For instance, collaborations between AI firms and hardware developers have led to robots capable of natural language processing and complex task execution. In contrast, domestic advancements, though later in inception, emphasize cost-effectiveness and application-oriented innovations, supported by policy initiatives and collaborative platforms. The following table summarizes representative international embodied robots, highlighting their features and technological highlights.
| Company | Release Time | Product | Characteristics |
|---|---|---|---|
| Tesla | 2024.10 | Optimus | Height 173 cm, weight 57 kg; uses deep learning for skill acquisition; interactive and adaptive to environments |
| Figure AI | 2024.08 | Figure 02 | Height 168 cm, weight 70 kg; integrates voice-to-voice models for human-like dialogue and task response |
| 1X | 2024.08 | Neo Beta | Height 167 cm, weight 30 kg; high mobility with 20-DoF hands; supports natural language processing |
| Boston Dynamics | 2024.04 | Atlas 2 | All-electric design; custom actuators for fluid motions; capable of complex maneuvers like standing and rotating |
| Agility Robotics | 2023.03 | Digit V4 | Height 175 cm, weight 65 kg; leg-based mobility with arms; designed for practical work tasks |
Domestically, embodied robot development has accelerated through a dual-track model involving enterprises and academic institutions. Policies have fostered innovation centers, such as the National Local Co-construction Embodied Intelligent Robot Innovation Center, which promotes data collection and model training. The table below illustrates key domestic embodied robots, showcasing their specifications and advancements in areas like mobility and AI integration.
| Company | Release Time | Product | Characteristics |
|---|---|---|---|
| Ubtech | 2024.10 | Walker S1 | Height 172 cm, weight 76 kg; 41 DoF; multimodal planning models for industrial tasks |
| Fourier Intelligence | 2024.09 | GR-2 | Height 175 cm, weight 63 kg; 53 DoF; 12-DoF dexterous hands for complex operations |
| Zhiyuan | 2024.08 | Expedition A2 | Height 169 cm, weight 69 kg; over 40 active DoF; multimodal model for interactive tasks |
| Yunchu | 2024.08 | Dr.01 | Height 173 cm, weight 85 kg; lightweight joints for flexibility; adaptive to complex environments |
| Xingdong Century | 2024.08 | Xiao Xing Max | Height 165 cm, weight 55 kg; 54 DoF; high-precision actions with array sensors |
Core technologies in embodied robots encompass hardware and software integration, driving their functionality and intelligence. Critical components include actuators, sensors, and control systems, which enable precise motion and interaction. For example, harmonic reducers provide high transmission ratios but face challenges in durability under frequent use. The efficiency of frameless torque motors is often modeled using equations like $$ \tau = K_t \cdot I $$ where \( \tau \) is torque, \( K_t \) is the torque constant, and \( I \) is current, but thermal management remains a key issue. High-performance controllers, combining CPUs and GPUs, support real-time processing; for instance, power consumption can be optimized through algorithms that minimize energy loss, expressed as $$ P_{\text{total}} = P_{\text{compute}} + P_{\text{motion}} $$ where \( P_{\text{compute}} \) and \( P_{\text{motion}} \) represent computing and motion power, respectively.
Environmental perception and scene understanding rely on multi-sensor fusion, such as vision systems, force feedback, and tactile sensors. These systems process data to build a coherent model of the environment, often using probabilistic methods for integration. A common approach involves Bayesian fusion: $$ P(S|D) = \frac{P(D|S) P(S)}{P(D)} $$ where \( P(S|D) \) is the posterior probability of scene \( S \) given data \( D \), enhancing decision-making in dynamic settings. However, delays in multi-modal data processing, typically around 200-300 ms, hinder real-time performance, necessitating algorithms that reduce latency below 100 ms for human-like responsiveness.
Gait control and dexterous manipulation are fundamental for stable movement and precise operations. Bipedal locomotion involves complex dynamics, often modeled using reduced-order approaches for simplicity. The equations of motion can be represented as $$ M(q) \ddot{q} + C(q, \dot{q}) + G(q) = \tau $$ where \( M \) is the mass matrix, \( C \) accounts for Coriolis forces, \( G \) for gravity, and \( \tau \) for joint torques. Reinforcement learning techniques optimize these models, enabling embodied robots to adapt gaits on uneven terrain. For upper-body manipulation, dexterous hands with multiple degrees of freedom allow tasks like grasping and assembly, controlled through force-feedback algorithms that adjust grip strength based on sensor inputs.
Embodied intelligence and large models form the cognitive core of embodied robots, enabling learning and adaptation. General large models, trained on diverse datasets, provide broad task capabilities, while vertical models specialize in specific skills like industrial assembly. The integration can be expressed through loss functions in training: $$ \mathcal{L} = \alpha \mathcal{L}_{\text{task}} + \beta \mathcal{L}_{\text{adapt}} $$ where \( \mathcal{L}_{\text{task}} \) is task-specific loss and \( \mathcal{L}_{\text{adapt}} \) penalizes deviations from adaptive behaviors. Embodied intelligence leverages physical interactions to refine these models, with simulation platforms generating synthetic data for training, reducing reliance on real-world experiments.
Human-robot collaboration and interaction focus on safe and intuitive cooperation. Emotion recognition systems use multi-modal data to adjust robot behaviors, enhancing user experience. Force control algorithms ensure gentle physical interaction, modeled as $$ F = K_p \cdot e + K_d \cdot \dot{e} $$ where \( F \) is the applied force, \( e \) is position error, and \( K_p \), \( K_d \) are control gains. This facilitates seamless teamwork in shared environments, though challenges persist in intent prediction and real-time adjustment.
Operating systems and toolchains provide the software backbone for embodied robots, supporting development and deployment. Simulation platforms, for instance, allow virtual testing of algorithms, accelerating innovation. The use of reinforcement learning in simulators can be described by the value function $$ V(s) = \mathbb{E} \left[ \sum \gamma^t r_t | s_0 = s \right] $$ where \( V(s) \) is the value of state \( s \), \( \gamma \) is the discount factor, and \( r_t \) is the reward at time \( t \). These tools enable large-scale parallel training, reducing development cycles and costs.
In special service environments, such as defense and rescue operations, embodied robots demonstrate unique advantages due to their human-like mobility and operability. They can navigate complex terrains, perform hazardous tasks like bomb disposal, and assist in disaster scenarios by delivering supplies or conducting searches. For example, in nuclear facilities, embodied robots equipped with sensors monitor radiation levels and handle equipment, reducing human exposure. The adaptability of these embodied robots in unstructured settings highlights their potential for life-saving applications, though endurance and reliability in extreme conditions require further enhancement.
In智能制造, embodied robots excel in flexible production lines, where they assist in assembly, welding, and material handling. Their bipedal design allows them to traverse human-centric spaces, integrating seamlessly into existing workflows. For instance, in automotive manufacturing, embodied robots perform bolt-tightening and part installation, improving efficiency and reducing manual labor. The use of AI enables skill imitation, where robots learn from human demonstrations, optimizing processes over time. However, challenges like precision in high-speed tasks and cost-effectiveness need addressing for widespread adoption.
Household and social services represent a growing domain for embodied robots, encompassing tasks like cleaning, companionship, and education. In healthcare, they aid in rehabilitation by guiding patients through exercises, while in retail, they provide customer service and logistics support. Emotional interaction capabilities, driven by AI, allow embodied robots to engage users naturally, fostering trust and acceptance. As populations age, these embodied robots could alleviate caregiver burdens, though issues around privacy and ethical use must be managed.
Despite progress, embodied robot development faces several challenges. Hardware-software co-design requires interdisciplinary integration, often complicating system optimization. For example, real-time processing demands efficient resource allocation, modeled as $$ \min \sum_{i} E_i \cdot t_i $$ where \( E_i \) is energy consumption and \( t_i \) is time for task \( i \), subject to constraints on computational limits. High-precision perception struggles with multi-modal fusion delays, while embodied intelligence needs stronger generalization for unpredictable environments. Power systems aim for longer续航, but battery technology lags, necessitating innovations in energy management. Additionally, standardization and safety frameworks are underdeveloped, posing risks in deployment.
Looking ahead, trends indicate deeper integration of embodied intelligence with general AI, enabling more autonomous and adaptive embodied robots. Multimodal large models will enhance decision-making, with vertical models specializing in niche applications. Simulation platforms will evolve to support massive parallel training, reducing development time. Computational advances will drive algorithm efficiency, facilitating real-time control. Moreover, ethical guidelines and safety standards will mature, ensuring responsible adoption. We believe that embodied robots will become integral to various sectors, transforming industries and daily life through continuous innovation.
In conclusion, embodied robot technology stands at the forefront of robotics, with rapid advancements in hardware, AI, and applications. By addressing current challenges and leveraging future trends, embodied robots can achieve greater autonomy and utility, ultimately contributing to societal progress and economic growth as a key element of embodied intelligence.