Physical AI
Physical AI
What is Physical AI?
Physical AI refers to Artificial Intelligence (AI) systems, which integrates both hardware (robots, sensors, drones) and software. In effect, Physical AI builds on traditional AI products by adding a physical embodiment through which the AI can act upon, sense and interact with the outside world.
At its core, Physical AI allows a machine to use the knowledge it gains from its interactions with its environment in order to create a real-time connection between the cognitive decision-making process and the physical actions of the machine. This includes technologies like autonomous mobile robots, industrial cobots (collaborative robots), AI-powered drones, humanoid robots, warehouse automation systems, and inspection robots used in hazardous or remote sites.
An important element of Physical AI is edge computing. Edge computing is a way of processing data at the source of its acquisition, instead of sending it all to a central cloud or data centre.
Traditional AI is primarily digital and focuses on activities such as performing calculations, analyzing large sets of data, generating predictions, or suggesting possible solutions to that data. It typically relies on external systems or human-operated machinery to interact with the physical world.
Physical AI, by contrast, can interact with their physical environments and perform physical tasks autonomously. Physical AI combines advanced algorithms with physical hardware, enabling systems to gather information through sensors, cameras, etc., move and operate in three-dimensional space, and act autonomously. While traditional AI mainly performs cognitive or analytical functions, Physical AI not only creates knowledge but also act upon the world around them. This makes it suitable for industries like construction, oil & gas, warehousing and manufacturing, where real world tasks and safety are critical.
Some real-world Physical AI examples include:
● viMac: An edge-AI anti-collision system for industrial vehicles (forklifts, cranes, dump trucks, etc.) that uses 360° vision to detect hazards, enforce speed limits, and alert drivers in real-time.
● viMov: A portable, self-contained AI safety monitoring device that works off-grid (no internet or power needed), detecting risks like PPE non-compliance, gas leaks, and dangerous zones in confined or remote spaces.
● viHoi: An AI-powered hoist monitoring system for cranes that uses computer vision to track load movement, detect personnel in danger zones, and raise alerts for safer lifting operations
How does Physical AI differ from traditional AI or software-only AI systems?
Physical AI differs from traditional AI as it combines intelligence with the ability to act in the real world.
In contrast to traditional AI that lives solely on computer servers or software as programs, Physical AI exists primarily in machines capable of physical interactions with their surroundings. Physical AI is thus not limited to analyzing information, making decisions based on processed data and providing insights; rather, it can perform tasks that require movement, lifting, visual inspection of materials, etc., utilizing robotics, drones, or other smart devices that can perform autonomously.
Further, edge computing allows Physical AI to analyze conditions locally and respond quickly and automatically to changes in the environment. This ability gives Physical AI an advantage in environments where working autonomously, safely, accurately and efficiently are critical, such as manufacturing, logistics, construction and energy industries.
In simple terms, traditional AI is “the brain”, while Physical AI is “the brain and the body”.
What are the key features of Physical AI?
The key features of Physical AI include:
● Real-Time Perception: The ability to make fast decisions based on information collected through cameras, sensors, LiDAR, etc., provides real-time perception for machines.
● Learning & Adaptability: Machines can learn from experience and adapt to changes in their environment.
● Edge Computing: Data is processed locally on the device, allowing immediate responses without relying on cloud processing capabilities.
● Autonomous Decision-Making: AI algorithms allow machines to independently process information and make informed decisions without human intervention.
● Safety & Reliability: Real-time monitoring and decision-making help support safe operations, especially in hazardous and dynamic environments.
● Mobility & Physical Action: Robots and machines can move, handle, manipulate, and interact physically with objects in the environment.
● Scalability: Physical AI systems can be deployed across multiple sites or tasks with minimal reconfiguration of systems.
● Predictive Capabilities: Machines can anticipate potential problems or risks and take preventive actions.
What technologies power Physical AI?
Key technologies powering Physical AI are:
● Artificial Intelligence (AI) & Machine Learning (ML): Enable machines to learn from data, make decisions, and understand patterns from those data.
● Computer Vision: Enables machines to “see” using cameras and interpret what they have seen – images, objects, and environments.
● Edge Computing: Processes data locally, enabling real-time decision-making without relying on cloud speed.
● Robotics & Mechatronics: Provide the physical components of machines, such as motors, actuators, robotic arms, wheels, and grippers, etc. for producing motion and performing actions.
● IoT (Internet of Things): Connects machines, sensors, and systems for smooth communication and coordinated operation.
● Sensors & Perception Systems: Devices like LiDAR, ultrasonic sensors, infrared sensors, GPS, and cameras help machines sense their environment.
● Digital Twins & Simulation Technologies: Create virtual models to improve accuracy, optimize performance, and predict issues before it occurs.
● Communication Technologies (5G, Wi-Fi, BLE): Allow fast, reliable data exchange between machines and monitoring systems.
What are the benefits of Physical AI?
The benefits of Physical AI are:
● Enhanced Safety: Robots and smart machines are capable of dealing with dangerous, heavy, or high-risk tasks, while simultaneously reducing risk of incidents for human workers.
● Real-time Monitoring & Visibility: Robots and IoT sensors provide organisations the ability to monitor and access data in real-time, helping them track operations and make smarter decisions.
● Real-Time Decision-Making: With edge computing and AI, machines can react instantly to changes in the environment, resulting in increased accuracy and control.
● Faster Response to Emergencies: Physical AI systems can be used to detect problems early, like leaks, fires, or equipment failure, and act immediately or notify teams.
● Greater Productivity: Physical AI can work continuously without breaks, helping organisations complete tasks faster and more efficiently.
● Increased Efficiency in Hazardous Areas: Physical AI can be deployed in areas of extreme heat, confined spaces, heights, or toxic environments where humans cannot work safely.
● Decrease in Operational Costs: Automation helps cut labor costs, reduce downtime, and minimize human error, leading to long-term savings.
● Support for Large-Scale Operations: Physical AI is ideal for mega-projects, warehouses, industrial plants, and smart cities where automation is essential for scale.
● Consistency & Quality: Machines deliver precise, repeatable results, improving overall quality in manufacturing, logistics, and construction.
● Human Workforce Support: Instead of replacing workers, Physical AI helps them by taking over repetitive or risky tasks, allowing humans to focus on skilled work.
RELATED TERMS
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Article by
Barnali Sharma
Content Writer
Barnali Sharma is a dedicated content contributor for viAct. A university gold medalist with an MBA in Marketing, she crafts compelling narratives, enhances brand engagement, and develops data-driven marketing campaigns. When she’s not busy working her content alchemy, Barnali can be found commanding stages with her public speaking or turning data into stories that actually make sense -because who said analytics can’t have a little creativity?