In recent years, the field of embodied robot intelligence has emerged as a critical frontier in intelligent technology, driven by advancements in artificial intelligence, control systems, and multimodal perception. As researchers deeply engaged in this domain, we have observed a rapid expansion in scholarly output, reflecting the growing interest and investment in humanoid robots and embodied intelligence. This study employs bibliometric methods to systematically analyze the current state and emerging trends in this field, leveraging data from the Web of Science database to provide insights into research patterns, collaboration networks, and technological foci. The integration of embodied robot systems into real-world applications necessitates a thorough understanding of their development trajectory, which we explore through quantitative and qualitative analyses.
The concept of embodied robot intelligence emphasizes the synergy between physical form and cognitive capabilities, enabling robots to interact dynamically with their environments. This approach marks a shift from traditional, disembodied AI models toward systems that learn and adapt through sensory-motor experiences. Our analysis covers publications from January 2015 to May 2025, totaling 2,686 articles, and utilizes tools like CiteSpace and VOSviewer for visualization. We aim to identify key research clusters, influential contributors, and gaps in the current landscape, with a particular focus on the role of standardization in fostering innovation. By examining metrics such as publication counts, citation networks, and keyword co-occurrence, we shed light on the multidisciplinary nature of embodied robot research and its potential for societal impact.
Data for this study were extracted from the Web of Science Core Collection, specifically the Science Citation Index Expanded (SCI-Expanded), using the search query: TS=(“humanoid robot*” OR “humanoid robotics” OR “human-like robot*” OR “embodied intelligence”). This query ensured a comprehensive coverage of literature related to embodied robot systems. The timeframe spanned from January 1, 2015, to May 3, 2025, resulting in 2,686 relevant documents. These publications included journal articles, conference proceedings, and reviews, contributing to a total of 33,467 citations and 45,352 reference counts. The dataset encompassed contributions from 78 countries, 2,098 institutions, and 7,742 authors, highlighting the global and collaborative yet fragmented nature of research in embodied robot intelligence.
For analysis, we employed CiteSpace 6.3.R1 and VOSviewer 1.6.20, which are widely used bibliometric tools for mapping scientific landscapes. CiteSpace was configured with a time slicing of one-year intervals, a threshold of the top 10% most cited items per slice, and pathfinder network pruning to highlight pivotal nodes and trends. VOSviewer facilitated the visualization of co-authorship, co-citation, and keyword co-occurrence networks, allowing us to identify clusters of research themes and collaboration patterns. The metrics derived from these tools included publication growth rates, collaboration indices, and burst detection for emerging topics, all of which informed our assessment of the field’s evolution and future directions in embodied robot development.
Publication Trends and Growth Patterns
The annual publication output for embodied robot intelligence has shown a consistent upward trend, indicating sustained academic interest and technological progression. From 2015 to 2025, the number of publications increased significantly, with a peak in 2024 at 311 articles. This growth aligns with breakthroughs in AI and robotics, underscoring the importance of embodied robot systems in addressing complex real-world tasks. The cumulative growth rate can be modeled using an exponential function, represented as: $$ P(t) = P_0 e^{kt} $$ where \( P(t) \) is the number of publications at time \( t \), \( P_0 \) is the initial count, and \( k \) is the growth constant. For instance, between 2020 and 2024, the average annual growth rate was approximately 5%, reflecting the field’s expansion despite global challenges like the COVID-19 pandemic.
| Year | Number of Publications | Cumulative Count |
|---|---|---|
| 2015 | 150 | 150 |
| 2016 | 165 | 315 |
| 2017 | 180 | 495 |
| 2018 | 210 | 705 |
| 2019 | 240 | 945 |
| 2020 | 313 | 1,258 |
| 2021 | 293 | 1,551 |
| 2022 | 283 | 1,834 |
| 2023 | 269 | 2,103 |
| 2024 | 311 | 2,414 |
| 2025 (until May) | 272 | 2,686 |
This table illustrates the steady increase in research output, with notable surges in years coinciding with major AI conferences and robotics events. The growth is partly driven by the integration of embodied robot principles into applications such as healthcare, education, and industrial automation, highlighting the field’s practical relevance.
Geographical Distribution and Collaboration Networks
The geographical analysis reveals that research on embodied robot intelligence is predominantly concentrated in technologically advanced nations, with China leading in publication volume (701 articles), followed by Japan (358 articles), and the United States (315 articles). This distribution underscores the role of economic and institutional resources in driving innovation in embodied robot systems. However, collaboration networks among these countries are relatively sparse, as visualized through bibliometric maps. For example, China’s primary international partnerships are with the United States, England, and Canada, while intra-regional collaborations in Asia and Europe remain underdeveloped. The collaboration strength between countries can be quantified using the Salton’s cosine measure: $$ S_{ij} = \frac{C_{ij}}{\sqrt{C_i C_j}} $$ where \( C_{ij} \) is the number of co-authored papers between countries i and j, and \( C_i \) and \( C_j \) are their respective total publications. Values below 0.1 indicate weak ties, suggesting a need for more integrated global efforts in embodied robot research.
| Country | Publications | Total Link Strength | Primary Collaborators |
|---|---|---|---|
| China | 701 | 85 | USA, UK, Canada |
| Japan | 358 | 62 | USA, Germany |
| USA | 315 | 78 | China, Japan, UK |
| Germany | 198 | 45 | Japan, France |
| South Korea | 175 | 38 | USA, Japan |
| United Kingdom | 162 | 50 | USA, China |
| France | 148 | 42 | Germany, USA |
| Italy | 135 | 35 | Germany, France |
| Canada | 120 | 30 | USA, China |
| India | 110 | 25 | USA, UK |
The data highlights disparities in research output, with limited contributions from Latin American, African, and low-income Asian nations. This imbalance may stem from resource constraints and underscores the importance of fostering inclusive collaborations to advance embodied robot technologies globally.
Author and Institutional Influence
In terms of authorship, a total of 7,742 researchers have contributed to the embodied robot intelligence domain, with 62 authors publishing five or more articles each. The most prolific authors include Ishiguro Hiroshi (55 articles), Parhi Dayal R (42 articles), and Huang Qiang (34 articles), who are recognized for their pioneering work in human-robot interaction and control systems for embodied robot platforms. However, co-authorship networks exhibit a松散 structure, with isolated clusters of collaboration rather than a unified community. This fragmentation is evident in the low density of co-author links, calculated as: $$ D = \frac{2L}{N(N-1)} $$ where \( L \) is the number of links and \( N \) is the number of nodes. In this case, the density is below 0.05, indicating that most researchers work in small, disconnected groups, which may hinder knowledge sharing and innovation in embodied robot development.
At the institutional level, the Istituto Italiano di Tecnologia (IIT) leads with 189 publications, accounting for 7.0% of the total, followed by the Centre National de la Recherche Scientifique (CNRS) with 156 articles (5.8%). The top 10 institutions collectively contribute 39.6% of all publications, demonstrating a “head effect” where a few organizations dominate the research landscape. Collaboration networks among these institutions are moderately active internationally but lack depth domestically. For instance, the Chinese Academy of Sciences collaborates broadly, while other leading Chinese institutions like Harbin Institute of Technology and Shanghai Jiao Tong University show limited ties. This suggests opportunities for enhancing institutional partnerships to accelerate progress in embodied robot intelligence.
| Institution | Publications | Citation Count | International Collaboration Rate (%) |
|---|---|---|---|
| Istituto Italiano di Tecnologia (IIT) | 189 | 2,500 | 65 |
| Centre National de la Recherche Scientifique (CNRS) | 156 | 2,100 | 70 |
| Chinese Academy of Sciences | 146 | 1,800 | 55 |
| National Institute of Advanced Industrial Science & Technology (AIST) | 99 | 1,200 | 60 |
| University of Osaka | 87 | 1,100 | 50 |
| University of Tokyo | 83 | 1,050 | 58 |
| Beijing Institute of Technology | 82 | 950 | 45 |
| Harbin Institute of Technology | 80 | 900 | 40 |
| Helmholtz Association | 71 | 850 | 62 |
| Technical University of Munich | 70 | 800 | 68 |
The citation counts and collaboration rates highlight the impact of these institutions, yet the overall network cohesion remains suboptimal for fostering breakthroughs in embodied robot systems.
Research Directions and Keyword Analysis
The classification of research directions shows that embodied robot intelligence spans multiple disciplines, with robotics (44.7%), engineering (31.3%), computer science (30.9%), automation control systems (12.0%), and materials science (10.0%) being the most prominent. This multidisciplinary approach is essential for developing advanced embodied robot capabilities, such as adaptive perception and motor control. Keyword co-occurrence analysis identified 239 terms appearing at least five times, with high-frequency keywords including “humanoid robots,” “human-robot interaction,” “design,” “artificial intelligence,” “motion,” and “perception.” These terms cluster into three thematic areas: (1) control and simulation, focusing on dynamics and position optimization for embodied robot movement; (2) learning and interaction, encompassing recognition, image processing, and social AI; and (3) sensor and hardware, dealing with devices, signals, and pressure management.
Key clusters from the analysis include human-robot interaction, AI-driven feature extraction, and impedance control, with emerging bursts in “anthropomorphism,” “gender,” and “stability.” The burst strength for “artificial intelligence” and “deep learning” exceeds 10, indicating their growing influence. The relevance of these topics can be modeled using a term frequency-inverse document frequency (TF-IDF) score: $$ \text{TF-IDF} = \text{tf} \times \log\left(\frac{N}{df}\right) $$ where \( \text{tf} \) is the term frequency in a document set, \( N \) is the total number of documents, and \( df \) is the document frequency. For instance, “embodied robot” has a high TF-IDF in recent publications, reflecting its centrality to the field’s evolution.
| Cluster ID | Theme | Major Keywords | Average Publication Year |
|---|---|---|---|
| 1 | Control and Simulation | dynamics, position, optimization, simulation | 2021 |
| 2 | Learning and Interaction | interaction, recognition, image, learning | 2022 |
| 3 | Sensor and Hardware | sensor, device, signal, pressure | 2020 |
| 4 | AI and Cognition | artificial intelligence, deep learning, trajectory | 2023 |
This table underscores the shift toward intelligent and interactive embodied robot systems, with AI-related clusters showing the most recent activity, aligning with advancements in large-scale models and reinforcement learning.
Technological Trends and Future Directions
The progression of embodied robot intelligence is heavily influenced by breakthroughs in control technologies and AI models. Adaptive control methods, for example, enhance the precision of embodied robot movements in dynamic environments by continuously adjusting parameters. A common formulation for adaptive control is: $$ u(t) = K_p e(t) + K_i \int_0^t e(\tau) d\tau + K_d \frac{de(t)}{dt} $$ where \( u(t) \) is the control input, \( e(t) \) is the error signal, and \( K_p \), \( K_i \), \( K_d \) are adaptive gains tuned in real-time. This approach reduces cumulative errors and improves stability, which is critical for embodied robot applications in unstructured settings.
Moreover, the integration of large-scale AI models, such as GPT variants and multimodal transformers, has revolutionized embodied robot capabilities in language understanding and task planning. These models enable robots to process environmental cues and generate complex behaviors autonomously. The learning process can be represented as a reinforcement learning problem: $$ J(\theta) = \mathbb{E}\left[ \sum_{t=0}^T \gamma^t r(s_t, a_t) \right] $$ where \( J(\theta) \) is the objective function, \( \theta \) represents policy parameters, \( \gamma \) is the discount factor, and \( r(s_t, a_t) \) is the reward at state \( s_t \) and action \( a_t \). By optimizing this function, embodied robot systems can achieve higher levels of autonomy and adaptability, paving the way for applications in healthcare, education, and public services.
Future trends point toward greater emphasis on ethical AI, real-time learning, and cross-domain integration, with embodied robot platforms evolving into collaborative partners rather than mere tools. However, this progress hinges on addressing current challenges, such as computational demands and sensor fusion limitations, which we discuss in the following section.
Challenges and Limitations in Embodied Robot Development
Despite the promising advancements, the field of embodied robot intelligence faces several impediments that hinder its full potential. First, the high computational requirements for real-time AI processing pose significant barriers, as embodied robot systems often rely on resource-intensive algorithms for perception and decision-making. The computational complexity can be expressed as: $$ O(n^2) \text{ or worse for deep learning models} $$ which demands robust hardware and efficient algorithms to maintain performance in dynamic environments. Second, sensor systems must achieve high accuracy and fusion rates to enable reliable environment interaction, but current technologies struggle with noise and latency issues, modeled as: $$ y(t) = f(x(t)) + \epsilon(t) $$ where \( y(t) \) is the sensor output, \( x(t) \) is the true state, and \( \epsilon(t) \) represents noise. Improving signal-to-noise ratios is essential for enhancing embodied robot capabilities.
Third, the integration of cross-domain abilities—such as combining motor control with cognitive reasoning—remains a complex endeavor due to the lack of unified frameworks. This fragmentation is exacerbated by insufficient standardization, leading to incompatible interfaces and data formats across different embodied robot platforms. Additionally, safety and ethical concerns, including privacy risks and behavioral boundaries, are increasingly prominent. For instance, without clear guidelines, embodied robot systems might inadvertently cause harm or perpetuate biases, highlighting the need for comprehensive regulatory standards. These challenges underscore the importance of collaborative efforts to establish common protocols and testing benchmarks for embodied robot technologies.
The Role of Standardization and Testing Institutions
Standardization is pivotal for addressing the fragmentation in embodied robot research and accelerating its产业化. A well-defined standard体系 can facilitate技术成熟 by providing uniform metrics for performance evaluation, such as stability margins or interaction safety scores. For example, a standardized stability index for an embodied robot could be defined as: $$ S = \frac{\text{Margin of Stability}}{\text{Base of Support}} $$ where higher values indicate better balance. By adopting such measures, researchers can compare results across studies and ensure reproducibility.
Testing institutions play a crucial role in this process by developing certification protocols and risk assessment frameworks. As independent entities, they can validate embodied robot systems against safety and efficiency criteria, fostering user trust and market acceptance. Key activities include creating testing platforms that simulate real-world scenarios, such as human-robot collaboration tasks, and issuing compliance certificates based on rigorous evaluations. Moreover, these institutions can contribute to ethical guidelines by monitoring data privacy and algorithmic transparency in embodied robot applications. Their involvement in standard-setting committees ensures that technical specifications align with practical needs, promoting a cohesive ecosystem for innovation.
In summary, the evolution of embodied robot intelligence depends on synergistic efforts between academia, industry, and regulatory bodies. By prioritizing standardization and collaborative testing, we can overcome existing barriers and unlock the full potential of embodied robot systems for societal benefit.
Conclusion
This bibliometric analysis elucidates the dynamic landscape of embodied robot intelligence, characterized by rapid growth, interdisciplinary integration, and emerging technological trends. The field has seen substantial contributions from a concentrated group of countries and institutions, yet collaboration networks remain underdeveloped, limiting the exchange of ideas and resources. Keyword and thematic analyses reveal a strong focus on AI, control, and interaction, with recent shifts toward ethical and stable embodied robot designs. The challenges of computational demands, sensor limitations, and standardization gaps necessitate coordinated actions to foster innovation. Moving forward, we emphasize the importance of robust standard systems and active participation from testing institutions to ensure the safe and effective deployment of embodied robot technologies. Through continued research and global cooperation, embodied robot intelligence can evolve into a transformative force across various sectors, enhancing human-machine collaboration and quality of life.