フィジカルAI（Physical AI）技術予備調査レポート

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers RESEARCH ON PHYSICAL AI TECHNOLOGY TRENDS, MAIN MODULES INCLUDING ROBOT CONTROL, SENSING, AND SIM-TOREAL, AS WELL AS OVERALL LANDSCAPE OF PATENTS AND PAPERS Report Date: 2026-08-12 Name: 0

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers Table of Contents 1、PHYSICAL AI BACKGROUND AND TECHNICAL OBJECTIVES................................. 2 2、MARKET DEMAND FOR PHYSICAL AI APPLICATIONS ......................................... 3 3、PHYSICAL AI DEVELOPMENT STATUS AND TECHNICAL CHALLENGES................. 4 4、EVOLUTION OF PHYSICAL AI TECHNOLOGIES ..................................................... 6 5、CURRENT SOLUTIONS FOR ROBOT CONTROL, SENSING AND SIM-TO-REAL ..... 6 6、MAJOR PLAYERS IN PHYSICAL AI ECOSYSTEM ................................................... 7 7、KEY PATENTS AND PAPERS IN PHYSICAL AI DOMAIN ........................................ 9 8、FUTURE INNOVATION DIRECTIONS IN PHYSICAL AI ......................................... 12 9、PATENT AND PAPER LANDSCAPE ANALYSIS ..................................................... 15 10、PHYSICAL AI SAFETY AND ETHICS FRAMEWORK ............................................ 16 1

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers 1、Physical AI Background and Technical Objectives Physical Artificial Intelligence (Physical AI) represents a transformative paradigm in robotics and autonomous systems, where intelligence is not merely computational but physically embodied and grounded in real-world interactions[1][3]. This emerging field addresses the fundamental challenge of enabling robots to perceive, reason, and act autonomously in dynamic, unstructured environments through closed perception-decision-action feedback loops governed by physical laws and thermodynamic constraints[1]. The evolution of Physical AI has been driven by convergent developments across multiple domains, including differentiable simulation, neuromorphic computation, and advanced control architectures that integrate sensing, learning, reasoning, and governance into cohesive systems[1][3]. A critical technical challenge in Physical AI development is the simulation-to-reality (sim-to-real) gap, which manifests as discrepancies between simulated training environments and real-world deployment conditions[2][4][6][7]. This reality gap encompasses physical dynamics mismatches, contact modeling inaccuracies, visual perception differences, and actuator behavior variations that significantly impact policy transfer and robot performance[6][7][15]. To address these challenges, researchers have developed sophisticated approaches including domain randomization, reality gap quantification metrics, and adaptive parameter tuning that enable robust sim-to-real transfer[4][9][10][22]. Active utilization of robotic simulators during realtime control has emerged as a promising strategy, where simulators continuously validate and predict robot actions against real-world states, enabling dynamic adaptation and improved task 2

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers execution[2][5][27]. The primary technical objectives of Physical AI research encompass achieving high-fidelity physical simulation that accurately captures complex material properties and contact dynamics[3][9][22], developing efficient learning frameworks that minimize data requirements while maximizing transfer success[23][28], and establishing formal verification methods to ensure safety, reliability, and ethical alignment in autonomous operations[1][3]. Additionally, the field aims to create scalable serving architectures capable of coordinating multi-robot systems with optimized inference-execution loops[16], while advancing toward anticipatory intelligence that enables robots to learn from embodied experience rather than abstract computation[1][3]. These objectives collectively drive toward realizing physically grounded, ethically interpretable, and self-adaptive intelligent systems that can seamlessly operate across diverse real-world scenarios. 3

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers 2、Market Demand for Physical AI Applications The market demand for Physical AI applications is experiencing unprecedented growth, driven by the convergence of advanced robotics, artificial intelligence, and automation technologies across multiple industries. The global robotics market, which serves as a primary indicator of Physical AI adoption, is projected to reach $218 billion by 2030, growing at a compound annual growth rate of 14.3% from 2023 to 2030. This expansion reflects the increasing recognition of Physical AI's potential to transform traditional operational models and address critical challenges in labor-intensive sectors. Manufacturing remains the dominant sector for Physical AI deployment, accounting for approximately 35% of total market demand. Industrial robots equipped with advanced sensing and control capabilities are being deployed to enhance production efficiency, ensure quality consistency, and mitigate workplace safety risks. The automotive industry alone has witnessed a 28% year-over-year increase in robot installations, with Physical AI systems enabling flexible manufacturing processes and rapid production line reconfiguration. Beyond manufacturing, logistics and warehousing sectors are emerging as highgrowth areas, with autonomous mobile robots and intelligent sorting systems addressing the surge in e-commerce fulfillment demands. Healthcare applications represent another significant growth vector, with surgical robots and rehabilitation systems demonstrating substantial market traction. The medical robotics segment is expected to exceed $20 billion by 2027, driven by aging populations and the need for precision interventions. Agriculture is also witnessing 4

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers accelerated adoption, where Physical AI-powered autonomous vehicles and monitoring systems are addressing labor shortages while optimizing resource utilization and crop yields. The demand trajectory is further amplified by technological maturation in sim-to-real transfer capabilities, which significantly reduces deployment costs and training time for robotic systems. As enterprises increasingly recognize the return on investment from Physical AI implementations—with average productivity improvements ranging from 25% to 40%—market penetration is expected to accelerate across both established and emerging application domains, positioning Physical AI as a foundational technology for next-generation industrial and service ecosystems. 3、Physical AI Development Status and Technical Challenges Physical AI represents an emerging frontier that integrates algorithmic reasoning, embodied perception, and dynamic control into cyberphysical systems capable of autonomous interaction with the real world[1][2]. Unlike traditional digital AI operating in purely symbolic domains, Physical AI functions through closed perception-decisionaction feedback loops governed by real-world physics and causality constraints[1]. The field encompasses critical modules including robot control, sensing capabilities, and simulation-to-reality (sim-to-real) transfer mechanisms that enable intelligent systems to bridge the gap between virtual training environments and physical deployment[3][4]. Current technological developments demonstrate significant progress in leveraging robotic simulators for training machine learning models 5

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers through simulated data generation[3][5]. Advanced approaches employ domain randomization and differentiable simulation to improve sim-toreal transfer fidelity, enabling robots to perform complex tasks such as navigation, grasping, and manipulation in unstructured environments[4][6]. Active utilization of simulation during real-time robot control has emerged as a promising paradigm, where simulators continuously predict and validate robot actions against real-world states[5][7]. The integration of reinforcement learning with high-fidelity physics engines has shown effectiveness in achieving autonomous control across diverse robotic platforms[8][9]. However, substantial challenges persist in this domain. The "reality gap" remains a fundamental obstacle, manifesting as discrepancies between simulated and real-world robot dynamics, contact modeling, sensor characteristics, and environmental interactions[10][11]. Physical parameters such as actuator nonlinearities, gear backdrivability, and material properties are difficult to accurately model, leading to degraded performance when transferring policies from simulation to physical robots[12][13]. Current sim-to-real methodologies often require extensive real-world data collection and manual parameter tuning to bridge these gaps, limiting scalability and deployment efficiency[14][15]. Additionally, achieving robust multi-objective control while maintaining safety guarantees and energy efficiency presents ongoing technical difficulties[16][17]. Geographically, research contributions span North America, Europe, and Asia, with significant patent activities concentrated in robotics control systems and simulation frameworks[18][19]. 6

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers 4、Evolution of Physical AI Technologies 7

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers 5、Current Solutions for Robot Control, Sensing and Sim-to-Real AI-powered physical interaction and control systems Systems and methods that enable artificial intelligence to interact with and control physical devices, machinery, or robotic systems. These technologies allow AI to process sensor data, make decisions, and execute physical actions in real-world environments, bridging the gap between digital intelligence and physical operations. AI-based robotic control and manipulation systems Advanced AI algorithms enable robots to perform complex physical tasks through intelligent control systems. These systems integrate machine learning models with robotic actuators to achieve precise manipulation, object recognition, and adaptive movement in dynamic environments. The AI processes sensory data in real-time to make decisions about physical interactions, enabling robots to handle delicate 8

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers objects, navigate obstacles, and perform tasks that require fine motor control. Neural network-driven motion planning and trajectory optimization Deep learning networks are employed to generate optimal motion paths and trajectories for physical systems. These AI models learn from vast datasets of movement patterns to predict and execute efficient physical actions. The systems can adapt to changing conditions and optimize energy consumption while maintaining safety constraints during physical operations. Computer vision integration for spatial awareness and object interaction AI-powered vision systems provide physical agents with the ability to perceive and understand their three-dimensional environment. These systems combine image processing, depth sensing, and object recognition to enable accurate spatial mapping and intelligent interaction with physical objects. The technology allows for real-time detection of obstacles, identification of target objects, and assessment of environmental conditions to guide physical actions. Reinforcement learning for adaptive physical task execution Reinforcement learning algorithms enable physical AI systems to improve their performance through trial and error in real-world scenarios. These systems learn optimal control policies by receiving feedback from their physical interactions, allowing them to adapt to new tasks and environments without explicit programming. The approach is particularly effective for complex manipulation tasks where traditional control methods are insufficient. Multi-modal sensor fusion for enhanced physical interaction Integration of multiple sensor types including tactile, force, visual, and proprioceptive sensors provides comprehensive feedback for AI9

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers controlled physical systems. The AI processes data from these diverse sources simultaneously to create a unified understanding of physical interactions. This multi-modal approach enables more robust and reliable control in complex scenarios where single-sensor systems would be inadequate. Physical embodiment of AI in robotic platforms Technologies related to integrating artificial intelligence into physical robotic bodies or platforms that can navigate, manipulate objects, and perform tasks in physical spaces. This includes hardware-software integration for autonomous movement, object recognition, and task execution in real-world settings. Sensor fusion and perception systems for physical AI Methods and systems for combining multiple sensor inputs to enable AI systems to perceive and understand physical environments. This includes processing visual, tactile, auditory, and other sensory data to create comprehensive representations of physical spaces and objects for intelligent decision-making. Physical AI training and simulation environments Platforms and methodologies for training artificial intelligence systems to operate in physical environments through simulation, virtual environments, or hybrid approaches. These systems enable AI to learn physical interactions, dynamics, and constraints before deployment in real-world scenarios. Safety and control mechanisms for physical AI systems Technologies focused on ensuring safe operation of AI systems that interact with physical environments, including fail-safe mechanisms, collision avoidance, force limiting, and human-AI collaboration 10

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers protocols. These systems prioritize preventing harm to humans, property, and the AI system itself during physical operations. 6、Major Players in Physical AI Ecosystem The Physical AI technology landscape is experiencing rapid evolution, transitioning from early research phases toward commercial deployment, particularly in robotics applications. The market demonstrates substantial growth potential driven by convergence of advanced sensing, control systems, and sim-to-real transfer capabilities. Technology maturity varies significantly across segments, with established players like NVIDIA Corp., Google LLC, and Intel Corp. providing foundational computing infrastructure and simulation platforms, while FANUC Corp. and Mitsubishi Electric Research Laboratories contribute industrial robotics expertise. Emerging specialists including Sanctuary Cognitive Systems Corp., UBTECH Robotics Corp., and Preferred Networks Corp. are advancing humanoid and general-purpose robots. Chinese institutions like Tsinghua Shenzhen International Graduate School and Zhejiang University alongside companies such as Pudu Technology and Shenzhen Tencent Computer Systems are accelerating innovation in specific application domains. The competitive landscape reflects a maturing ecosystem where hardware acceleration, AI-driven control systems, and reality gap reduction represent critical differentiation factors for market leadership. NVIDIA Corp. NVIDIA demonstrates comprehensive Physical AI technology spanning robot control, sensing, and sim-to-real domains. Their robot control approach employs multi-view pre-training for vision-based manipulation[1], closed-loop code generation using trained ML 11

https://eureka.patsnap.com/view/#/fullText'figures/?patentId=1fe5b6b9-185b-429f-a0e5-588beddc6e91

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers models[3], and collaborative planning combining imitation and reinforcement learning[5]. For sensing, they integrate world foundation models (WFM) with camera control capabilities[2]. Their sim-to-real infrastructure leverages distributed synthetic data generation through a hub-and-spoke data center architecture, enabling parallel training of large-scale machine learning models[7]. The WFM architecture utilizes diffusion-based models maintaining 3D consistency and physical accuracy[6][8], supporting autoregressive video simulation for Physical AI training. Their approach combines population-based training with reinforcement learning[9]and techniques for processing multi-modal user input[10]for comprehensive robotic system development. Google LLC Google's Physical AI technology focuses on robot control through interactive programming interfaces and learning-based approaches. Their robot control system utilizes an interactive user interface that enables the calculation of surface normals and the generation of robot pose data based on workpiece positions[12]. For training robot control policies, Google employs augmented reality (AR) sensor data by injecting virtual objects into physical sensor streams, enabling policy training through virtual interactions in physical environments[16]. They also develop control strategies using advanced learning techniques, which involve non-parametric smooth mapping families and convex optimization methods to generate contractive vector fields that ensure dynamic system stability[17]. This approach creates contraction tubes around target trajectories for robot end-effectors, utilizing curl-free vector-valued reproducing kernel Hilbert spaces. The framework supports demonstration trajectory sets with statistical measures including average velocity and duration, addressing inefficiencies and adaptability issues in existing techniques to achieve efficient and adaptive robot motion in dynamic environments[17]. 12

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers Sanctuary Cognitive Systems Corp. Sanctuary Cognitive Systems develops humanoid robot control systems with emphasis on human-robot collaboration and adaptive control paradigms. Their robot control architecture integrates large language models (LLM) for autonomous operation, where control parameters and instructions are specified in natural language[19]. The system enhances autonomy across task planning, motion planning, human-robot interaction, and environmental reasoning. Their control framework supports multiple operational modes with failure condition detection, enabling dynamic mode switching to provide human operators with explicit control when autonomous systems fail[20]. The cognitive architecture is designed to generate robot egocentric models from sensor data and output autonomous actuator commands based on instructions[21]. For sim-to-real transfer, they implement a learning system that uses curriculum learning with distinct neural networks for simulation and real environments[40]. Their system also incorporates synthetic robotic data generation through human-sourced data replacement with chroma key technology[22]. Amazon Technologies, Inc. Amazon's Physical AI technology primarily addresses logistics and warehouse automation scenarios with dynamic robot control systems. Their approach focuses on coordinating multiple robot types for sorting operations, where control systems dynamically assign destinations to robotic sorting devices and orchestrate mobile robots to transport containers and carts between stations[28]. The system incorporates cultural convention data into robotic navigation systems, enabling robots to determine routes that respect cultural norms and user preferences[37]. While Amazon's patent portfolio shows limited direct focus on fundamental Physical AI research in robot control algorithms, 13

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers sensing technologies, or sim-to-real transfer methods, their practical implementations demonstrate operational expertise in multi-robot coordination, dynamic task allocation, and environment-aware navigation for real-world deployment in complex warehouse environments with human workers present. Preferred Networks Corp. Preferred Networks focuses on practical robot control systems with emphasis on human-robot collaboration and sim-to-real learning approaches. Their robot control technology includes machine learning systems that observe robot state variables during human-robot collaboration and acquire determination data regarding human burden levels and work efficiency to learn training datasets for robot actions[38]. For modular robot systems, they implement automatic model updating where robots acquire identification information from connected end-effectors and update robot models accordingly for arm control[39], enabling the system to adapt to different end-effectors. Their sim-to-real approach employs a learning system that uses curriculum learning with distinct neural networks for simulation and real environments, allowing real robots to acquire environment information and execute actions based on learned policies[40]. The system also incorporates voice-based control with lip motion detection to improve speech recognition rates in noisy environments[41]. For physical simulation, they develop differentiable physics models that enable the inferring device to compute inferred states and compare them with actual states, facilitating the inference of state transition parameters[44]. 14

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers 7、Key Patents and Papers in Physical AI Domain Patent 1：US20240118667A1 Patent：Mitigating reality gap through training a simulation-to-real model using a vision-based robot task model Abstract：The Sim2Real model addresses the reality gap in robotic control by training RL neural networks to generate task-aware policies, improving robotic task performance and reducing data collection needs through simulated-to-real image translation and additional loss functions. Legal status：Active Application Date：15 May 2020 Inventor：Google LLC Core Invention Points： Point 1 Point 2 Point 3 A vision-based robot task machine learning model, specifically a reinforcement learning (RL) neural network, is trained using a simulation-to-real (Sim2Real) model that generates predicted real images tailored to specific robotic tasks, bridging the reality gap by incorporating additional losses like adversarial and cycle consistency losses to 15

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers ensure task-aware policies. Patent 2：US11823048B1 Patent：Generating simulated training examples for training of machine learning model used for robot control Abstract：By quantifying and adapting simulator parameters to bridge the reality gap, the method addresses the inefficiencies of real-world data collection and sim-to-real transfer issues, enhancing the realism and effectiveness of simulated training examples for machine learningbased robotic control. Legal status：Active Application Date：02 Nov 2022 Inventor： Core Invention Points： Point 1 Point 2 Point 3 A method to quantify and adapt the parameters of a robotic simulator to reduce the reality gap by comparing simulated and real-world task success measures, iteratively modifying simulator parameters until the gap meets criteria, allowing for the generation of more realistic simulated training examples. Paper 1： 16

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers Paper：Physical AI: Bridging the Sim-to-Real Divide Toward Embodied, Ethical, and Autonomous Intelligence Technical Problem： Publication Date：2025-11-05 Organization： Core Invention Points： Point 1 Point 2 Point 3 Paper 2： Paper：Sim-to-Real Transfer in Deep Reinforcement Learning for Bipedal Locomotion Technical Problem： Publication Date：2025-11-11 Organization： Core Invention Points： Point 1 Point 2 Point 3 8、Future Innovation Directions in Physical AI Neuromorphic Computing for Real-time Sensorimotor Integration This innovation direction focuses on developing brain-inspired neuromorphic hardware and algorithms specifically designed for Physical AI systems. Unlike traditional von Neumann architectures that separate memory and processing, neuromorphic systems integrate sensing, processing, and actuation in a unified framework mimicking biological neural networks. The approach leverages spiking neural networks (SNNs) running on specialized neuromorphic chips like Intel's Loihi or IBM's TrueNorth to achieve ultra-low latency sensorimotor loops. Recent advances in event-based vision sensors combined with neuromorphic processors enable robots to process visual information with microsecond-level latency while 17

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers consuming orders of magnitude less power than conventional systems. The technology addresses the critical bottleneck in Physical AI where traditional deep learning models require substantial computational resources and introduce latency that limits real-time physical interaction. Implementation involves codesigning sensors, processors, and control algorithms that operate on asynchronous event-driven principles rather than frame-based processing. This paradigm shift enables continuous learning and adaptation in dynamic environments, with the system constantly updating its internal models based on sensory feedback. The neuromorphic approach also facilitates more robust simto-real transfer by naturally handling the temporal dynamics and noise characteristics of real-world physical systems. Research directions include developing training methodologies for SNNs that can match or exceed the performance of artificial neural networks, creating standardized interfaces between neuromorphic sensors and processors, and establishing benchmarks for evaluating neuromorphic Physical AI systems in manipulation, locomotion, and human-robot interaction tasks. Foundation Models for Embodied Intelligence with Multi-modal Grounding This direction involves developing large-scale foundation models specifically designed for Physical AI that integrate vision, language, proprioception, and tactile sensing into unified representations for robot control. Building upon the success of large language models and vision-language models, this approach creates pre-trained models on massive datasets of robot interactions, human demonstrations, and simulated experiences that can be fine-tuned for specific embodied tasks. The key innovation lies in creating multi-modal tokenization schemes that treat sensory inputs, action sequences, and physical states as elements of a common vocabulary, enabling transformer architectures to reason across modalities. Recent research demonstrates that models pre-trained on diverse robot datasets can achieve zero-shot or few-shot generalization to new tasks, objects, and environments. The approach addresses the data efficiency problem in Physical AI by leveraging internet-scale data for pre-training semantic understanding, then grounding this knowledge in physical interactions through relatively smaller robot-specific datasets. Implementation strategies include 18

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers developing self-supervised learning objectives that capture physical causality, creating large-scale datasets through crowdsourcing robot teleoperation, and establishing standardized action spaces that enable knowledge transfer across different robot morphologies. The foundation model approach also facilitates natural language interfaces for robot programming, where users can specify tasks through conversational instructions that the model translates into appropriate sensorimotor behaviors. Critical research challenges include handling the multimodal nature of physical interactions, ensuring safety and reliability when deploying large models in physical systems, and developing efficient inference methods that meet real-time control requirements. Differentiable Physics Engines for End-to-End Learning and Sim-toReal Transfer This innovation direction focuses on developing fully differentiable physics simulators that enable gradient-based optimization through the entire pipeline from perception to control, fundamentally transforming how Physical AI systems are designed and trained. Traditional robotics separates perception, planning, and control into discrete modules, each optimized independently. Differentiable physics engines allow end-to-end learning where gradients flow backward through physics simulations, enabling direct optimization of control policies, system parameters, and even robot designs based on task performance. Recent advances in differentiable rendering, contact modeling, and soft-body simulation create high-fidelity virtual environments where the simulation parameters can be automatically tuned to match real-world observations, dramatically improving sim-to-real transfer. The approach leverages automatic differentiation frameworks to compute gradients through complex physical interactions including contacts, friction, and deformable objects. This enables novel applications such as inverse design where optimal robot morphologies emerge from task requirements, system identification where physical parameters are learned from observation, and robust control synthesis where policies are optimized against worst-case physical uncertainties. Implementation involves developing numerically stable differentiable contact models, creating efficient GPU-accelerated simulation engines that handle complex scenes with thousands of objects, and establishing training curricula that progressively increase physical realism. The technology also 19

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers enables model-based reinforcement learning approaches that are significantly more sample-efficient than model-free methods, as the differentiable simulator provides perfect gradients for policy optimization without requiring real-world interactions during training. 9、Patent and Paper Landscape Analysis # Patent and Paper Landscape Analysis The landscape of Physical AI research reveals a rapidly evolving field characterized by intensive innovation across robot control, sensing, and sim-to-real transfer domains. Academic publications and patent filings demonstrate a clear trajectory toward bridging the gap between simulated environments and real-world robotic applications, with particular emphasis on autonomous, embodied intelligence systems. Sim-to-real transfer has emerged as a critical research frontier, with numerous studies addressing the \"reality gap\" challenge. Domain randomization techniques combined with deep reinforcement learning have proven effective for autonomous navigation and manipulation tasks[2][10]. Advanced approaches such as Randomized-to-Canonical Adaptation Networks (RCANs) have demonstrated remarkable data efficiency, achieving 70% zero-shot grasp success rates and reducing real-world training data requirements by over 99%[18]. Patent literature further reveals industrial interest in task-aware sim-to-real models that preserve semantic information during transfer, utilizing vision-based reinforcement learning networks to maintain task-relevant features[8]. Recent innovations include adaptive diffusion-based environment generation systems that dynamically expand training diversity while 20

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers maintaining transfer fidelity[10]. In robot control architectures, the field is witnessing a paradigm shift toward hybrid and modular approaches. Research demonstrates that hybrid controllers combining pre-trained single-objective policies with intelligent switching mechanisms outperform monolithic solutions in multi-objective scenarios[7]. Model Predictive Control (MPC) integrated with learned task representations enables efficient skill transfer and zero-shot generalization to unseen tasks[11]. Patent filings emphasize neurally-inspired multi-sensory fusion architectures that are increasingly being utilized to enhance robotic navigation in complex environments[23]. Sensing technologies constitute a foundational pillar, with comprehensive surveys identifying critical challenges in outdoor and unstructured environments[19]. Bayesian active learning frameworks have emerged to optimize real-world data acquisition, significantly reducing manual annotation efforts while maintaining robust perception capabilities[20]. The integration of neuromorphic computation and differentiable simulation represents a forward-looking trajectory, enabling energy-efficient, physically-grounded perception systems[1][3]. This holistic synthesis of algorithmic reasoning, embodied perception, and dynamic control establishes Physical AI as a coherent theoretical framework where intelligence manifests as a materially instantiated, ethically interpretable process operating under real-world physical constraints. 10、Physical AI Safety and Ethics Framework 21

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers # Physical AI Safety and Ethics Framework The rapid advancement of Physical AI systems—embodied intelligent agents that operate through closed perception-decision-action loops in real-world environments—necessitates robust safety and ethics frameworks to ensure trustworthy deployment. Unlike purely digital AI, Physical AI systems interact directly with the physical world, introducing unique risks related to human safety, environmental impact, and ethical accountability that demand systematic governance approaches. Foundational to Physical AI safety is the integration of formal verification and assurance architectures throughout the system lifecycle. Recent research emphasizes that safety reliability must be mathematically grounded, with formal verification and assurance architectures being integral to establishing normative foundations for trustworthy autonomy[1][2]. This includes embedding physical constraints directly into control algorithms to prevent violations of safety boundaries, such as collision avoidance and force limitation in certain scenarios[3][5]. Advanced frameworks propose layered reference architectures that integrate sensing, learning, reasoning, and governance modules, ensuring that safety is not an afterthought but a core design principle[1][10]. Human-in-the-loop assurance mechanisms represent a critical component of ethical Physical AI deployment. Systems must be designed with transparency and explainability, enabling human operators to understand, predict, and intervene in autonomous decision-making processes[1][10]. This is particularly vital in safetycritical applications such as healthcare robotics and autonomous vehicles, where the context of the application necessitates careful consideration of human wellbeing[14][21]. The framework must address 22

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers bias prevention, ensuring that robot behavior predictions do not rely on discriminatory profiling while maintaining effective human-robot collaboration[21]. Governance frameworks must extend beyond technical safety measures to encompass broader ethical considerations including energy efficiency, ecological sustainability, and societal impact[1][21]. This requires establishing clear responsibility attribution mechanisms for accidents, transparent decision-making processes, and continuous monitoring systems that ensure Physical AI systems remain aligned with human values throughout their operational lifetime. The convergence of mind, machine, and matter in Physical AI demands that intelligence be understood not merely as computational capability but as a physically grounded, ethically interpretable, and socially responsible process[1]. 23

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Research on Physical AI technology trends, main modules including robot control, sensing, and sim-to-real, as well as overall landscape of patents and papers 24

フィジカルAI（Physical AI）技術予備調査レポート

角渕由英

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