IntroductionSocial robots, as a fusion of artificial intelligence and human-computer interaction, are reshaping the collaborative patterns of human society. Through biomimetic design and multimodal perception technologies, these intelligent agents can not only analyze language and recognize emotions but also autonomously adapt to dynamic scenarios, transitioning from mechanical tools to “social partners.” In recent years, with breakthroughs in affective computing and large language models, social robots are rapidly integrating into fields such as healthcare, education, and home services, becoming innovative solutions to social issues like aging and autism.1 Definition and Application Scenarios1.1 DefinitionA social robot is an autonomous intelligent agent capable of establishing emotional connections with humans through natural interaction and possessing social intelligence. Its essence lies in understanding social contexts, expressing emotions, and exhibiting adaptive behaviors through biomimetic design, multimodal perception, and artificial intelligence technologies. Unlike industrial robots, the core goal of social robots is not merely to perform physical tasks but to become “collaborative partners” for humans in emotional support, education, and healthcare scenarios.1.2 Application ScenariosSocial robots have penetrated the following fields:(1) Healthcare: Assisting social training for children with autism, providing companionship during postoperative recovery;(2) Public Services: Airport guidance, intelligent consultation in bank lobbies;(3) Home Scenarios: Elderly companionship, educational assistance for children;(4) Commercial Fields: Personalized recommendations in retail locations, hotel services.2 Core Technology OverviewThe core technology system of social robots integrates innovations from multiple fields, including artificial intelligence, multimodal interaction, affective computing, and hardware engineering. The technological development path is illustrated in the following diagram, reflecting a stepwise breakthrough from perception navigation to social dexterity and coordinated design.
Core Technology Development Path of Social Robots(Image source: Journal of Medical Internet Research)2.1 Natural Language Processing and GenerationThe language interaction capability of social robots relies on Natural Language Understanding (NLU) and Natural Language Generation (NLG) technologies.The NLU module achieves precise semantic parsing through intent recognition, entity extraction (such as time and location in user requests), and sentiment analysis (to assess user emotions). Key technologies include LSTM-based sequence modeling and Transformer architecture for contextual understanding.Dialogue management employs either a pipeline architecture or an end-to-end architecture. The former includes dialogue state tracking (DST) and policy learning (DPL), while the latter relies on large language models (such as ChatGPT) to directly generate coherent responses.Generative AI, represented by ChatGPT, significantly enhances the creativity and personalization of open-domain dialogues through a pre-training and fine-tuning paradigm. For example, Microsoft’s Xiaoice achieves long-term emotional conversations through three modules: “core chatting, visual perception, and skill expansion.”2.2 Affective Computing and Multimodal InteractionAffective intelligence is the core feature that distinguishes social robots from traditional tools. Key technologies include:l Affective modeling: Achieving multimodal emotion recognition through facial expression recognition (such as the OpenFace algorithm), voice emotion analysis (tone and speech rate feature extraction), and body language perception (fusion of motion sensor data).l Affective generation: Dynamically adjusting the robot’s response strategy based on an emotion state machine (Emotion FSM) or deep reinforcement learning (DRL). For instance, the social robot Pepper can convey empathy through eye contact and changes in tone.l Multilingual support: Combining cross-lingual pre-trained models (such as mBERT) with real-time translation technology to achieve barrier-free interaction in multilingual scenarios.2.3 Autonomous Decision-Making and Adaptive LearningSocial robots need to achieve autonomous behavior planning in dynamic environments. Related technologies include:l Reinforcement Learning (RL): Optimizing dialogue strategies through algorithms like Q-learning, for example, adjusting communication methods based on patient feedback in healthcare companionship scenarios.l Transfer Learning: Rapidly adapting to new domains (such as educational tutoring robots) using pre-trained models (like BERT) under small sample data conditions.l Lifelong Learning: Continuously updating user profiles through memory networks to avoid the “catastrophic forgetting” problem.2.4 Knowledge Management and Personalization SystemsKnowledge graph construction: Integrating structured knowledge bases (such as medical guidelines) with unstructured data (social media corpora) to support factual Q&A and contextual services.User profiling: Implementing interest mining and behavior prediction based on collaborative filtering (CF) and deep representation learning (such as Graph Neural Networks), for example, customizing interaction rhythms for children with autism.2.5 Hardware and System Integrationl Sensor fusion: Enhancing the safety of physical interactions through 3D vision (such as Intel RealSense), tactile feedback (flexible electronic skin), and environmental perception (LiDAR) technologies.l Embedded systems: Ensuring real-time responses through edge computing (such as NVIDIA Jetson) and lightweight model deployment (such as TensorRT).l Modular design: Optimizing the body structure through 3D printing and semiconductor processes, balancing functional expansion and cost control.3 Development HistoryThe development history of social robots can be divided into four main stages, each accompanied by technological breakthroughs and iterative upgrades in application scenarios. The following outlines the specific evolution path and representative events.(1) Theoretical Emergence Period (1950-1990): Early explorations in human-computer interaction1950: Alan Turing proposed the “Turing Test” in his paper “Computing Machinery and Intelligence,” exploring the philosophical question of whether machines possess human intelligence, laying the theoretical foundation for social robots.1966: MIT developed the world’s first chatbot, Eliza, which simulated psychological counseling dialogues based on rule matching. Despite its simple functionality, it pioneered human-machine language interaction.1970: Japanese roboticist Masahiro Mori proposed the “Uncanny Valley Theory,” revealing the psychological acceptance curve of humans towards anthropomorphic robots, profoundly influencing the design of social robots’ appearance and behavior.(2) Technological Exploration Period (2000-2010): Algorithm breakthroughs and product prototypes1997: The introduction of LSTM (Long Short-Term Memory networks) solved the gradient vanishing problem of recurrent neural networks (RNNs), significantly enhancing machines’ ability to process sequential data and providing key technological support for natural language understanding.2005: The academic community officially defined “Socially Assistive Robotics,” clarifying its core goal of providing support through non-physical social interactions, such as educational companionship and rehabilitation assistance.Early 2010s: SoftBank launched the NAO and Pepper robots, integrating voice interaction and basic emotional recognition functions, becoming representatives of early commercialized social robots.(3) Application Expansion Period (2015-2020): Multimodal interaction and deepening scenarios2014: Microsoft released the social chatbot XiaoIce, achieving coherent dialogues of over 30 turns by integrating EQ (emotional intelligence) and IQ (cognitive intelligence), significantly enhancing user engagement.2016: Research focus shifted to multilingual understanding and affective computing, for example, perceiving user emotions through visual, auditory, and tactile multimodal data and generating human-like responses.2018-2020: Social robots penetrated fields such as healthcare and education, with products like Paro (a therapeutic seal robot) used for dementia care and Kismet robots enhancing children’s interactive experiences through facial expressions.(4) Intelligent Breakthrough Period (2020-Present): Generative AI and full-scenario penetration2022: The emergence of large language models like GPT-3 enabled robots to possess contextual understanding and creative content generation capabilities, such as writing poetry and generating personalized suggestions.2023: ChatGPT sparked a wave of generative AI, pushing social robots towards “autonomous content generation” while triggering discussions on misinformation detection and ethical regulation.2024: Humanoid robots like Sophia and Ameca simulate human social behaviors through high-precision micro-expressions and body language, applied in complex scenarios such as customer service and entertainment.4 Industry Policies and Market Scale4.1 Industry PoliciesAs a fusion field of artificial intelligence and robotics technology, the development of social robots heavily relies on policy guidance and industry ecosystem support. In recent years, China has constructed a multi-level policy system from national top-level design to local practice, promoting technological innovation, industry chain collaboration, and market application expansion.(1) National Top-Level Strategic PlanningAt the national level, the robotics industry is positioned as a strategic emerging industry, with a series of plans clarifying development paths. For example, “Made in China 2025” (2015) lists robotics as one of the top ten key areas, proposing breakthroughs in core technologies such as intelligent control and human-computer interaction; the “Robot Industry Development Plan (2016-2020)” emphasizes the penetration of service robots into family, education, and healthcare scenarios, establishing special support for R&D and industrialization; the “New Generation Artificial Intelligence Development Plan” (2017) promotes the deep integration of artificial intelligence and robotics, supporting key technological breakthroughs in emotional interaction and multimodal perception for social robots; the “14th Five-Year Plan” for robotics industry development (2021) further proposes the “Robot+” application demonstration action, encouraging the implementation of social robots in elderly companionship and educational services in people’s livelihood fields.(2) Local Policies and Industry SupportLocal governments have introduced supporting policies based on regional advantages, accelerating industrial agglomeration through funding subsidies and park construction. The Pearl River Delta and Yangtze River Delta regions have taken the lead in establishing robotics industrial parks, providing tax incentives and R&D subsidies to attract leading companies to layout in the social robot sector. Cities like Beijing and Shanghai have released special policies supporting basic research in human-computer interaction and affective computing, promoting cooperation between universities and enterprises in industry-university-research collaboration. The central and western regions have reduced production costs for social robots by undertaking the hardware manufacturing segment through “industrial transfer guidance policies.”4.2 Market ScaleThe social robot market is in a phase of rapid growth, driven mainly by the surge in demand for emotional companionship, breakthroughs in generative AI technology, and the expansion of multi-scenario applications. According to Beijies Consulting, the global companionship robot market size is projected to reach 75 billion yuan in 2023, expected to reach 304.3 billion yuan by 2029, with a compound annual growth rate (CAGR) of 25.56% from 2024 to 2029. Grand View Research predicts that the global chatbot market size will reach 27.297 billion USD by 2030, with a CAGR of 23.3% from 2023 to 2030; Statista estimates that the market size will exceed 1.2 billion euros by 2025, highlighting short-term explosive potential.Products represented by AI companionship robots achieve a fusion of emotional interaction and practical functionality through large model empowerment. For example, generative AI technologies like ChatGPT enhance content generation capabilities, driving robots to transition from tool-like to “social partner” roles. Social robots are accelerating their implementation in scenarios such as rehabilitation assistance and psychological counseling. For instance, the psychological counseling robot “Personal Intelligence” from Infection Company has already shown commercial potential.5 Development Trend AnalysisFrom a technological perspective, the social robot industry is accelerating towards multi-scenario integration and deepening intelligence. Generative artificial intelligence technology, centered around large models, continues to break through, pushing chatbots from single dialogue functions into vertical fields such as intelligent manufacturing, healthcare, and education. The application of pre-trained models like ChatGPT significantly enhances robots’ autonomous content generation capabilities, with future breakthroughs focusing on personalized interaction, emotional simulation, and the dynamic balance of ethical boundaries. Meanwhile, key technologies such as affective computing, natural language processing, and sensing technologies are rapidly iterating. Patent analysis shows that technological hotspots are concentrated on fine-grained optimization of human-computer interaction and adaptation to user needs. Additionally, the proliferation of low-cost hardware design and open-source software lowers technical barriers, promoting the inclusive development of social robots. Overall, technological development presents a three-dimensional trend of “large model-driven scenario expansion + generative AI deepening content capabilities + hardware cost reduction.”From a competitive perspective, the global market exhibits characteristics of “accelerated regional differentiation, concentration of leading players, and coexistence of long-tail innovation.” Europe and the United States dominate the high-end market due to their technological first-mover advantage, while China occupies a leading position in niche markets such as catering and home services, relying on large-scale manufacturing capabilities and scenario implementation advantages. Leading manufacturers consolidate their positions through vertical integration of large model technologies and industry data barriers, while small and medium-sized enterprises seek breakthroughs through differentiated scenarios (such as emotional companionship and customized education) and open-source ecosystems. In the future, as the application layer of large models explodes, the competitive focus will shift from single technical indicators to a multi-dimensional game of “ethical compliance + user experience + cross-industry ecological cooperation,” with the industry expected to present a competitive landscape of “technology-scenario-capital” overlay.ReferencesOpen Source Securities Research Report (2025), “Emotional Companionship Demand + Large Model Empowerment, AI Companion Hardware Welcomes a Bright Future”Journal of Agricultural Library and Information Science (2024, Issue 3), “Concepts, Tasks, and Applications of Social Robots in Information Behavior Research”Research on Science and Technology Management (2024, Issue 9), “Research on the Development Trends of Social Robots from a Multi-Granularity Patent Perspective”Unmanned Systems Technology (2023, Issue 2), “Analysis of the Impact of ChatGPT on the Technological Development of Social Robots”Robotics (2022, Volume 11), “A Survey on Recent Advances in Social Robotics”International Journal of Environmental Research and Public Health (2022, Volume 19, Issue 3), “Empathy in Human–Robot Interaction: Designing for Social Robots”Cybersecurity (2021, Issue 4), “Visual Analysis of Social Robot Research Based on Knowledge Graphs”Journal of Medical Internet Research (2021, Volume 23), “Views on Using Social Robots in Professional Caregiving: Content Analysis of a Scenario Method Workshop”Science and Technology and Engineering (2016, Issue 12), “Current Status and Key Technology Research of Social Robot Development”END| Source: Zhongfu Venture Capital| Statement: We value originality and are happy to share. If there are any copyright issues with the content and images, please contact us promptly, and we will address them immediately.