Embodied AI vs. physical AI: Why their differences matter

Comparing physical and embodied AI

Physical AI is a broad, generic term that encompasses all AI entities designed to interact with the real world. Physical AI uses artificial intelligence, mechanical robotics and spatial computing to perceive, reason and act within our physical world. Physical AI receives and ingests real-world data from many sources, such as cameras, microphones, LiDAR (light detection and ranging) and an array of IoT sensors. It processes this data to understand a situation, plans responses through reasoning, acts on those plans and learns from the results.

Despite its name, physical AI doesn't technically require a mechanical form. Typical examples of physical AI include autonomous vehicles, but AI systems that analyze real-world data, such as predicting maintenance requirements, are also under the broad umbrella of physical AI. By comparison, AI systems that analyze business data or make stock predictions wouldn't be considered physical AI.

Despite its name, physical AI doesn't technically require a mechanical form.

Embodied AI specifically integrates intelligence into a mechanical or physical body. Intelligence might be located within the body itself through edge computing and edge AI. It could also be available remotely using network connectivity to exchange data and perform reasoning with AI platforms operating in distant data centers.

Embodied AI might readily use edge and remote approaches together. The body includes sensors and actuators that enable the embodied AI to learn, navigate and interact with its real-world environment. It uses varied methodologies, including touch, spatial perception, and trial-and-error to plan and move within the real world.

While embodied AI is considered a subset of physical AI, not every physical AI is necessarily embodied.

Use cases for physical and embodied AI

There are countless uses for AI that's integrated into real-world situations. Here are some of the more common physical and embodied AI applications.

Predictive analytics

AI systems, with or without physical bodies, can gather and analyze real-world information from sensors and other devices and systems generating real-time data. This capability lets physical AI predict and automatically schedule maintenance on vehicles, machinery and other real-world devices. Other uses include analyzing and optimizing traffic patterns based on traffic load as well as road and weather conditions.

Manufacturing

Specialized robotic devices, such as high-precision welding units, articulated assembly devices, and inspection and quality control cameras, can build and assemble a product and ensure its quality in real time while continuously adjusting and calibrating themselves. These robots have a physical presence but not "bodies" in the traditional autonomous sense.

Logistics

Increasingly, autonomous mobile robots represent a class of guided vehicles using real-time sensor data and AI processing for dynamic pathing, sorting and inventory interactions such as picking and loading. This capability can be useful when warehouse layouts and inventory assortments frequently change.

Autonomous vehicles

Self-driving vehicles, flying drones and other autonomous machinery can use computer vision, LiDAR and other spatial data to perceive the environment and rely on predictive analytics to navigate complex road systems to reach remote destinations.

Healthcare

Robotic systems, like articulated surgical robots, can perform surgeries under human control and sometimes autonomously. Increasingly intelligent prosthetics, such as cybernetic arms and legs, can help patients move and rehabilitate after serious injuries.

Human-like robots

Embodied AI can take countless forms to accomplish different tasks, but humanoid robots are also evolving to perform services that cater to human interaction, including hotel reception, hospital information, basic janitorial tasks, home assistants, and teaching and education tasks.

Physical and embodied AI implementation challenges

Bringing AI into the physical world is one of the more challenging aspects of modern robotics. Adoption requires overcoming several complex data-centric issues, including the following:

• A messy real world. AI is rarely trained in the real world because of a high risk for damage, injury or worse. Consequently, AI is trained using simulations such as virtual layouts of factory and warehouse floors. But virtual simulations don't always translate perfectly into real-world environments, resulting in unexpected or unpredictable outcomes.
• Data limitations for real-world conditions. AI can access vast amounts of data from corporate stores and even the internet, but there's no established data for a real physical environment. Any data regarding the physical environment must be created, stored and processed, which is time-consuming, error-prone and expensive.
• Complex integration stack. Moving AI into the physical world requires extremely complex integrations of sensors, actuators, edge computing, wireless networking and varied mechanisms that demand smooth integration with software and business systems.
• Safety concerns. It's one thing when an AI system makes a faulty prediction and quite another when it makes a mistake in the physical world. A warehouse robot exceeding weight limits or an autonomous vehicle lost in a confusing environment can cause unpredictable outcomes and accidents. Accounting for edge cases and handling them gracefully is a major safety issue in design and deployment.
• Limited human skill sets. Despite the hype, robots aren't just drop-in replacements for humans. Robots must be deployed, trained, managed and maintained, requiring skilled human expertise in engineering and software. Businesses that lack skilled staff will struggle with robotics deployments.

Physical and embodied AI adoption strategies

There's no single path to move AI into the real world. The deployment steps and speed varies radically with the choice of physical or embodied AI, the type of industry, and technical and regulatory demands. For example, using an AI system to gather IoT information from manufacturing machines to make preventive maintenance predictions is significantly different than deploying warehouse robots or even fielding a fleet of autonomous vehicles. Physical and embodied AI adoption is generally done in four phases.

1.     Preparation and assessment

Robotics adoption starts with a careful assessment of current infrastructure and needs, including a comprehensive workflow evaluation to recognize and analyze the physical tasks that will be affected. Pay close attention to tasks that are unpredictable or highly variable because automation might not be suit certain tasks. Evaluate the existing infrastructure from the data center to the edge. Understand the edge computing capabilities, know sensor data composition and flow, and check the network for adequate performance, including bandwidth and latency to ensure the infrastructure can support the data movement and processing demands of the robots being deployed. Consider testing methodologies, such as digital twins, that can be used when training AI models before deployment.

2.     Test deployment and refinement

Physical and embodied AI deployment isn't an all-or-nothing proposition and should be carefully planned. Start with limited testing and proof-of-concept evaluations in isolated, single-task situations within well-controlled environments where mistakes or oversights will have little impact on operations or human safety. Consider demonstration-based training and direct the robots in their intended tasks, which is often easier, faster and less error-prone than writing complex instructions. Focus on safety issues, such as ensuring compliance standards, defining operational limits and establishing emergency measures like kill switches.

3.     Scale and increase complexity

As test deployments prove concepts and demonstrate value, systematically increase the complexity and importance of the tasks, continuing to monitor performance and safety. Add different robots as required to scale up the fleet of embodied AI devices. Some deployments might shift from dedicated models for single-purpose robots to flexible foundation models for more task flexibility and reasoning potential. Optimize learning routines so AI systems can adapt and improve their behaviors with little or no human guidance. Some form of extended data sharing can let diverse AIs share and obtain information from across the enterprise.

4.     Monitor, optimize and monetize

Use metrics  like success rate and consistency to track physical and embodied AI performance. Objective metrics can be used as a basis for additional training and task refinements. AI ROI can be a challenge to measure precisely because the money saved with AI systems can be reallocated to maintenance and human upskilling. When determining ROI, consider all the factors involved in physical and embodied AI investments and costs.

Stephen J. Bigelow, senior technology editor at TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.