Physical AI deployment challenges and how to address them

Physical AI use cases

Physical AI use cases are broad and diverse, typically deployed in industries where critical real-time data affects real-world conditions. They include the following:

• Factory robots. From articulated arms to fully embodied AI, robots work on factory production lines to build products, such as microelectronics, vehicles and other heavy durable goods. Factory robots use sensors to collect data and manage interactions with the real world. That data is analyzed and used to refine and tune a robot's operations. Factory robots might operate independently as cobots in concert with human colleagues.
• Inspection and quality control. Computer vision systems can inspect raw materials before manufacturing and product defects during manufacturing. They perform a wide range of inspection tasks, such as inspections of buildings, hazardous sites, submersible systems and heavy equipment, while mitigating risks to humans.
• Defense. Autonomous robots are invaluable on the battlefield, gathering intelligence, delivering supplies, evacuating wounded combatants and making enemy contact. These robots use myriad cameras, data, sensors and motive systems to move heavy loads across unpredictable and treacherous terrain.
• Logistics. Fully autonomous robots are well suited for warehouse and logistics work, picking, lifting, moving, packing, loading and storing inventory in manufacturing and order fulfillment environments. At the same time, other physical AI systems can track inventory using RFID, mobile scanning robots and other scannable tag technologies to monitor stock and coordinate ordering materials.
• Autonomous vehicles. Physical AI embraces an ever-expanding assortment of autonomous vehicles -- each can be termed embodied AI -- that perform various tasks. Driverless cars can transport people, while driverless trucks can carry goods and materials packed by robots in warehouses or logistics hubs. Autonomous delivery systems are dedicated robots that deliver packages to waiting customers. Autonomous tractors and other heavy machinery can analyze terrain to optimize operation and minimize fuel use. All these vehicles use real-time GPS, weather and road data, along with visual data from cameras, radar, LiDAR and other sensors to complete their tasks.
• Agriculture. Embodied AI can use smart tractors and other dedicated field robots to monitor plant growth and soil health, requesting irrigation and soil management as required. At the same time, embodied AI robots can distinguish crops from weeds, performing weeding operations to improve crop health while reducing pesticide and herbicide use.
• Healthcare. Ever-improving robotic surgery systems can perform delicate procedures with precision, typically under the guidance of a skilled human surgeon. An orthopedic robot can use AI and preplanned 3D mapping data to align bones and perform precise drilling. These systems use cameras and other sensors to navigate human physiology, adjust to real-time patient data and monitor patient status through a wide range of patient sensors.

Physical AI implementation challenges

Building, implementing and managing physical AI can pose significant hurdles for businesses, including real-world unpredictability, data issues, compute limitations and physical safety concerns.

The real world is unpredictable

AI models process information and make decisions in an ever-changing physical environment. Layouts, maps, temperatures, lighting, surfaces and surrounding objects are always changing, making it extremely difficult to perceive and reason reliably under all conditions. It's the game of exceptions: Physical AI systems can operate flawlessly and progress faster in controlled lab environments when conditions are known and unchanging, but frequently fail in production because the environment's unstructured nature introduces risks and complexities the AI is unprepared to handle. Extensive reinforcement learning and high-performance safety responses are needed.

Training and operational data are limited

Databases and the internet can provide a wealth of knowledge for AI systems, but not for physical sensations and responses such as walking on uneven terrain or handling certain objects. Much of the data needed to train and operate physical AI systems must come from direct human training, which can still slow down and limit training. Data available for niche industrial environments and workflows can be expensive and subject to critical IP and privacy concerns.

Computing challenges limit performance and integration

In edge computing, the physical AI system must remain safe through high-priority monitoring and responses and must perceive and infer in real time without conflicts or excess latency. Both demands pose significant compute problems. Also, integrating modern AI stacks with traditional, sometimes aging, operational infrastructures can be costly and technically challenging.

There's no clear standard for governance and safety

Along with the lack of a defined regulatory safety standard, there's no accountability for final execution authority if the AI can't make a decision or makes an incorrect choice. In addition, most organizations lack liability, governance or accountability mechanisms to address physical AI failures or unintended outcomes. Safety should focus on systems that are secure, interoperable and resilient to protect human safety and life against disruptions and malicious actions.

Cost is a major barrier

Business owners must carefully consider the total cost of ownership (TCO) of any physical AI project. TCO starts with the initial equipment costs but must also include facility retrofits for new equipment, sensors and robots; integration with current systems; alignment with current workflows; ongoing maintenance and replacement parts; and productivity lost due to downtime for physical AI implementation and troubleshooting. These costs can vastly exceed the initial investment in AI and hardware. Businesses that overlook comprehensive TCO assessments risk project delays and even abandonment.

Best practices for physical AI implementation

There's no single universal approach to implementing physical AI. Designs and goals can be as diverse as the organizations themselves. But the following general practices can help organize and streamline physical AI deployments:

• Set clear goals for the physical AI project. Design the project with specific and measurable goals for performance, speed, safety or other business needs. Choose objective metrics to gauge those goals, draw a baseline, then measure and compare the changing metrics against the baseline to measure the project's success.
• Focus on data quality. Physical AI projects can be limited when using real data that can be difficult and time consuming to obtain. Data quality must be a top concern. Real-time data should ideally require little, if any, preprocessing. In edge computing situations, sensors should deliver reliable, accurate and complete data in a format well-suited to AI ingestion and processing -- even on the embodied AI itself.
• Watch for data and model drift. Data drift occurs when the statistical properties of input data change over time, while model drift is a decline in a model's predictive accuracy over time. Both types of drift are normal since real-world conditions inevitably change. Use ongoing statistical testing to check data drift and continuously monitor model performance to guard against model drift.
• Approach physical AI deployment in phases. Physical AI is not an all-or-nothing proposition. It can be a complex undertaking and should be approached in carefully orchestrated phases. A phased approach limits investment, mitigates risk, builds expertise and identifies overlooked issues. Start with lab development, move to limited zone deployment and move to company-wide deployment when the project is mature and issues are understood and resolved.
• Approach physical AI autonomy in phases. Don't just push a button, walk away and expect perfection. Start with low-priority deployments and use direct control, shared control or teleoperation (remote human operation) to limit physical AI autonomy until reliability and performance are validated. Then, systematically increase autonomy and continue refining the physical AI operation.
• Make safety a core design criterion. Include the consistent use of safety devices such as physical barriers to confine robots to certain areas, AI kill switches and physical emergency stop buttons, speed limits, alert sounds and lights to warn humans of robot proximity.
• Employ digital twin technology. A digital twin is a virtualized replica of a physical object, system or process constantly updated with real-world data. Use digital twins and simulations to develop path planning and consider potential edge cases. It's a powerful way to find limits, test data sets and spot possible failures before deploying real hardware in the field.
• Always keep humans in the loop. No matter how well designed and mature, a physical AI project will encounter conditions that cause unusual or unexpected errors, mistakes, damage and physical injury. Assign clear ownership and establish direct escalation paths for physical AI errors. This approach can be as simple as managing or replacing sensors, performing routine maintenance, making repairs and containing unexpected issues like spills or inventory damage caused by unexpected errors.

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