Physical AI: Building the Next Foundation in Autonomous Intelligence

Introduction

The world is moving towards an Autonomous Economy which represents a transformative economic model where AI, edge computing, robotics, spatial intelligence, and simulation technologies work together to enable systems to operate autonomously with minimal human intervention. Physical AI represents the convergence of these technologies enabling computers to sense, understand, predict, and act with the physical world, creating unprecedented opportunities for customers to embrace the transition towards this Autonomous Economy (https://www.linkedin.com/pulse/path-fully-autonomous-economies-andre-drpde/). Physical AI underpins the paradigm shift to autonomous operations and advances traditional AI systems that operate purely in digital environments to intelligent systems that can perceive, understand, and act in the physical world. This technology set is transforming everything from transportation (self-driving cars) to manufacturing (lights-out manufacturing facilities) to energy (minimal humans on-site and automated inspections of hazardous areas) to healthcare (minimally invasive robotic surgeries) and much more. In a prior blog, AWS proposed a 4-level Physical AI capability spectrum which describes WHAT levels of autonomy Physical AI can enable. Now we will provide guidance on HOW to achieve these levels of autonomy. An example can be found in the following blog featuring Diligent Robotics for healthcare.

In this blog, we describe a holistic Physical AI framework as a blueprint to chart your course towards automation. The framework breaks down Physical AI from an abstract concept into practical concrete capabilities that can be developed and integrated into your technical development roadmap. It addresses use-cases today and prepares you to solve challenges tomorrow. It describes a continuous learning loop that connects the physical world (atoms) to the digital world (bits) that accelerates development of autonomy in physical operations. Lastly, we clarify the difference between Physical AI model training in the virtual world and real-time autonomous operations in the physical world and explain how the two are connected in cloud to edge hybrid deployment. This blog is the first introductory blog in a planned multi-blog series where we will dive deep into each of the capabilities in the Physical AI framework.

Understanding Physical AI

At AWS, we define Physical AI as a system of hardware and software that integrates perceiving, understanding, reasoning, and learning to interact with the physical world. Physical AI is a subset of artificial intelligence that focuses on understanding spatiotemporal relationships and the physical nature of the world and then interacts with their surroundings through sensors and actuators. It processes multimodal inputs including images, videos, text, speech, depth/lidar, and real-world sensor data, to derive insights and enable real-time decision-making in autonomous systems capable of operating independently in complex, dynamic environments. For example, an AI model can use reasoning to describe how to pour a cup of coffee, whereas a Physical AI model first reasons where the coffee is located and that it needs to be poured in the cup, and then extends additional capabilities into the physical world to identify, grasp, lift, and pour a cup of coffee in real world conditions.

AWS’s Physical AI Framework

To fully realize the potential of Physical AI, organizations need a systematic approach that addresses the entire lifecycle of autonomous systems. The AWS Physical AI conceptual framework shown in Figure 1 provides this comprehensive structure through six interconnected capabilities that creates a continuous learning cycle between digital intelligence and physical action. This zooms in to six capability areas covered in end-to-end Physical AI tech stack, also covered in this blog. We will first describe each of these capabilities and then discuss how these capabilities are used to build and connect the training loop in the virtual world and the autonomy loop in the physical through hybrid cloud-to-edge deployment. In this manner, Physical AI represents an evolution toward systems that reason about future states, plan complex action sequences, and continuously improve their physical capabilities.

Figure 1: Diagram showing the Physical AI continuous learning loop with the training loop, autonomy loop, and 6 key capabilities identified.

1. Connect & Digitize the Physical World: The foundation of any Physical AI system lies in its ability to capture and digitize real-world information. IoT devices, sensors, cameras, and other physical devices collect multimodal state data from physical environments. Spatial sensors such as LiDAR can map depth and volumetric data, geospatial and satellite data are used to map large physical areas, and sensors enabling monitoring of parameters such as temperature, humidity, and chemical composition. These various data create a comprehensive digital representation of your physical world through 1D data streams, 2D images, 3D point clouds, sensor data, and metadata from enterprise Operational Technology (OT) systems and asset management systems. This rich sensory input forms the foundation upon which all subsequent AI processing depends. At AWS, we offer a range of services to enable this data collection including Amazon IoT SiteWise, Amazon IoT Core, Amazon Kinesis Video Streams, which can be used as part of the Industrial Data Fabric and Smart Machine solution guidance, as well as partner solutions such as Matterport, Treedis, and Prevu3D for collecting 3D data.

2. Store & Structure Data: Physical AI systems employ a dual-pathway architecture: low-latency sensor data streams directly to edge ML models for immediate reactive control, bypassing network and use real-time operating systems (RTOS), while higher-level reasoning tasks leverage cloud-connected knowledge graphs and enterprise system integration (ERP, CRM, LIMS, PLM) to enable complex planning and decision-making. The structured approach ensures that unstructured, diverse data types can be efficiently processed and correlated. Effective data management in Physical AI systems requires handling massive volumes of information from multiple sources while maintaining real-time parsing. Advanced storage architectures and data processing pipelines enable organizations to manage this complexity while ensuring that critical information remains available for both immediate decision-making and long-term learning. At AWS, we offer a variety of services such as Amazon S3, Amazon DynamoDB and Amazon Aurora for storage, and Spatial Data Management on AWS solution for managing complex spatial, IT, and OT data.

3. Segment & Understand Data: This stage handles data manipulation including transformations, cleaning, and temporal resampling of sensor streams, converting video, LiDAR, and time-series data into structured 3D models and environmental representations that inform simulation workflows. It transforms raw, multi-modal physical world data into AI-ready insights through pre-processing and relationship mapping. Critical to this process is building ontological relationships between disparate multi-modal datasets through knowledge graphs, enabling systems to connect data such as maintenance manuals via RAG, catalog pre-made 3D assets, and establishing semantic connections across spatial, operational, and temporal data dimensions. AWS services power this transformation: AWS Glue provides serverless ETL capabilities with built-in data transformation pipelines for processing and synchronizing multi-modal sensor data, while Amazon Neptune enables sophisticated knowledge graphs and ontologies that structure spatial relationships and asset metadata, creating the foundational intelligence layer that autonomous systems require to understand and interact with physical environments. Look at this blog for an example of a framework for segmenting and understanding data.

4. Simulate, Train & Optimize Models: Simulation environments provide safe, controlled spaces for training autonomous systems without real-world risks, supporting development of Physical AI systems across multiple use cases. These environments enable comprehensive training for model development targeting near-edge deployment, where AI systems can learn from countless scenarios, including rare or dangerous situations that would be impractical or impossible to test in reality. These simulation capabilities encompass Digital Twins and include: simulation-based training and virtual testing, synthetic data generation for model development, training and tuning of both ML and hybrid AI + mechanistic models, and development of models optimized for edge deployment. These simulation environments enable iterative optimization of Physical AI models, allowing teams to refine perception, decision-making, and control algorithms while validating performance across diverse scenarios before edge deployment. The simulation capabilities range from basic digital representations to world physics models (NVIDIA Omniverse, Unity, Unreal Engine, and emerging WFMs) to high-fidelity engineering simulations (Computational Fluid Dynamics, Finite Element Analysis, Thermodynamic Process Modeling). See this example of running NVIDIA Cosmos world foundation model on AWS. These Physical AI models can potentially be represented by Digital Twins which are a complex topic for which we developed the L1-L4 Digital Twin Leveling Guide and the Digital Twin Framework reference Architecture. AWS offers a range of services including AWS Batch, AWS ParallelCluster, AWS Parallel Computing Service, Amazon SageMaker, and Amazon EKS/ECS for building, orchestrating, and training models.

5. Deploy & Manage Autonomous Systems: Once trained and validated, AI models and policies must be deployed to autonomous systems with robust management capabilities. This capability handles over-the-air updates, agent policy management, and ongoing system updates to ensure that deployed systems remain current, compliant, and effective. The deployment phase requires careful consideration of edge computing capabilities, localized infrastructure, network connectivity, and security requirements. Systems must be able to operate reliably even when disconnected from central management systems while maintaining the ability to receive updates and report status information. AWS IoT Greengrass serves as the core edge runtime that enables secure deployment and management of AI models and applications on autonomous systems, supporting over-the-air updates, local processing capabilities, and the ability to operate reliably even when disconnected from central management systems. AWS IoT Device Management complements this by providing fleet-wide operations including remote device monitoring, policy management, and automated over-the-air firmware updates, while AWS Systems Manager enables centralized management of edge devices alongside traditional IT infrastructure for tasks like OS patching and application deployments. Additionally, AWS IoT Core facilitates secure bidirectional communication between autonomous systems and the cloud, enabling real-time status reporting and policy updates, while services like AWS Secrets Manager and IoT Device Defender ensure robust security and compliance management across deployed autonomous fleets.

6. Edge Inference & Operations: The final capability brings intelligence to the edge. Edge-based computing allows for lower-latency data transfer, enabling real-time analysis of on-device computing that drives actuators and sensor arrays without network dependency. Physical AI systems require immediate responses for critical applications such as collision avoidance in autonomous vehicles or emergency shutdowns in industrial equipment, where even milliseconds matter and network connectivity cannot be relied upon. Deploying sophisticated reasoning capabilities to edge devices presents significant challenges in model performance optimization, ultra-low latency inference, and operation under unreliable connectivity, representing a key investment area for AWS to enable advanced Physical AI models on resource-constrained edge hardware. AWS offers services such as AWS IoT Greengrass which enables local AI inference with ultra-low latency even when disconnected from the cloud, while AWS Local Zones and AWS Outposts to extend cloud capabilities to remote locations, ensuring that AI processing occurs locally to reduce network dependency.

The Flywheel Effect: Continuous Reasoning Improvement Through Operation

What makes the Physical AI framework particularly powerful is its data-driven improvement potential. As autonomous systems operate in the real world, they generate operational data that can inform refinements to Physical AI models. Enhanced models enable more capable autonomous systems, which in turn generate additional training data, creating a feedback loop that can drive capability improvements while reducing operational costs. This learning cycle means that Physical AI systems have the potential to become more effective over time, though the degree of improvement depends on the nature of the environment and the quality of data collected. Each interaction with the physical world provides new training data, edge cases, and optimization opportunities. Organizations that successfully implement this framework can expect their systems to demonstrate improving performance, reliability, and efficiency as they accumulate operational experience, particularly when combined with strategic model management and human oversight.

Dual-Loop Architecture: Cloud and Edge Integration

The framework operates through two critical loops that work in concert to deliver comprehensive Physical AI capabilities. The Training Loop operates primarily in cloud environments, handling data processing, AI model training, and simulation activities. This loop leverages the computational power, storage capacity, and globally distributed networking infrastructure to develop and refine AI capabilities. The Autonomy Loop focuses on real-time operations and physical world interactions, typically operating at the edge where autonomous systems are deployed. This loop prioritizes speed, iteration, and reliability, ensuring that systems can respond to changing conditions without depending on network connectivity to cloud resources. The integration of these two loops enables organizations to benefit from both the computational advantages of cloud infrastructure and the responsiveness requirements of edge deployment. Data flows seamlessly between cloud and edge environments, ensuring that learning and improvement occur continuously while maintaining operational reliability.

Deploying Autonomous Operations Securely

Security forms the foundational backbone of AWS’s Physical AI framework, where autonomous systems must operate with unwavering trust and resilience across every stage of the digital-to-physical loop. As organizations deploy AI agents that orchestrate relationships between physical systems, digital intelligence, and human oversight, AWS provides the security framework that enables secure autonomous operations from edge to cloud. The Physical AI framework inherently requires enterprise integration and security compliance to be prioritized throughout the entire workflow – from initial sensor data capture and spatial data management, through AI model training and physics-based simulation, to autonomous system deployment and real-time edge inference operations. Autonomous systems planned to operate in secure/sensitive environments or interact with people need security consideration for things like proprietary/secret world data and environments, and personally identifiable information (PII). AWS’s security posture applies to Physical AI by securing multi-modal data flows, protecting AI training pipelines for Physical AI models, ensuring secure operation of Digital Twins, and secure connection loop between physical systems and digital brains with optional encryption in-transit as well as encryption at-rest. This security-first approach enables customers to confidently develop autonomous systems that can perceive, understand, and act in the physical world while maintaining the highest standards of data protection, access control, and operational integrity, ultimately allowing enterprises to realize the transformative potential of Physical AI while safeguarding their most critical assets and operations.

Building the Autonomous Economy

The Physical AI framework provides organizations with a roadmap on their journey towards autonomous operations, helping them contribute to and benefit from the emerging autonomous economy. By implementing approaches that address the full lifecycle of autonomous systems, from initial data collection through ongoing operations and improvement, organizations can develop sustainable competitive advantages in their respective domains.

Success in Physical AI requires more than just deploying individual technologies; it demands a systematic approach that integrates sensing, processing, learning, and action into cohesive systems capable of operating independently in complex environments. The framework outlined here provides the structure necessary to achieve this integration while ensuring secure autonomous operations, scalability, reliability, and continuous improvement.

Conclusion

AWS’s Physical AI framework represents a fundamental shift in how organizations can securely build and deploy autonomous systems that bridge the digital and physical worlds. By integrating six interconnected capabilities—from connecting and digitizing the physical world through edge inference and operations—this framework creates a continuous improvement flywheel where real-world operational data drives increasingly capable AI models. The dual-loop architecture combining cloud-based training with edge-based autonomy, enables organizations to develop systems that can understand, reason, and act in complex physical environments with minimal human intervention. This approach is already transforming industries from manufacturing and transportation to energy and healthcare, powering the emerging Autonomous Economy where intelligent systems operate independently while continuously learning and improving. Check out the complimentary podcast: Physical AI: Teaching Machines to Act, Not Just Think.

Ready to explore how Physical AI can transform your operations? Join us for our upcoming 6-part blog series where we’ll dive deep into each capability of the Physical AI framework, sharing detailed technical guidance, reference architectures, and real-world customer examples. Whether you’re just beginning your autonomous systems journey or looking to scale existing deployments, ask to Connect with our Physical AI specialists today to discuss your specific use case and discover how AWS can help you participate in the Autonomous Economy.