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What physical AI safety covers
“Physical AI” is a broad description, not the scope of a single robot-safety standard. A system may combine an AI model with an industrial robot, end effector, sensors, control software, loads, workcell, and human procedures. A safety assessment should account for those elements together, including foreseeable misuse and non-routine work such as setup, programming, adjustment, recovery, and maintenance.
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The standards below address different parts of that picture; none should be treated as a universal approval of every physical AI system.
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|---|---|---|
| ISO 10218-1:2025 | Requirements for the industrial robot itself. The 2025 edition is the third edition, published in February 2025. | Application and integration requirements are addressed in Part 2. Part 1 excludes several uses, including medical and healthcare robots, public-access service robots and consumer products, and systems for lifting or transporting people. Check the applicable Part 2 edition, local adoption, and sector-specific rules. |
| ISO/TS 15066:2016 | Supplementary guidance for collaborative industrial robot operation alongside ISO 10218-1 and ISO 10218-2. ISO reports that it was reviewed and confirmed in 2022 and remains current. | It applies to the industrial robot systems described by ISO 10218-1 and ISO 10218-2, not non-industrial robots. Its principles may be useful in other areas, but do not establish that a non-industrial application is covered or certified. |
| NIST AI Risk Management Framework (AI RMF) 1.0 | Voluntary, non-sector-specific guidance for managing AI risk, including context-sensitive safety, robustness, reliability, testing, monitoring, and potential human intervention. | It is not a robot-safety standard and does not replace applicable machinery or sector-specific requirements. NIST published version 1.0 in 2023; check for revisions before relying on it. |
A vendor’s use of “cobot” does not show that a particular collaborative application is safe. The task, tool, workspace, human activity, and protective functions all matter.
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A practical risk-reduction sequence
1. Define the application and who could be exposed
Document the intended task, operating modes, work envelope, robot, end effector, loads, nearby machinery, environmental conditions, and who may enter the area. Include expected and foreseeable abnormal work: programming, testing, setup, adjustment, recovery, and maintenance. OSHA notes that robot accidents often occur during these non-routine conditions, but its overview does not provide a general accident rate to apply to a particular site.
2. Assess hazards across the integrated system
Consider the robot and the application together, including hazards from tooling and associated equipment. Assess how people can reach the work area, what the robot can do in each mode, and what happens when equipment or procedures do not behave as intended. OSHA’s robotics resources point to risk-assessment and integration guidance; ISO 10218 separates robot requirements from application and integration requirements.
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3. Select safeguards for the identified hazards
Choose safeguards based on the risk assessment, the task, and the access people need. The options below are different approaches, not interchangeable features; a combination may be appropriate. A warning light or sound on its own is not a safeguard.
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4. Validate the safety functions and integration
Verify that sensors, safety-related control functions, robot stopping behavior, tooling, layout, and procedures work together under expected and reasonably foreseeable conditions. Check coverage of the full work envelope, including the end effector, and what happens if a sensor, control, or communication link fails. A perception function or sensing device alone does not demonstrate that the complete system is safe.
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5. Manage AI-related risk through deployment
Use an AI risk process alongside machinery and sector-specific safety requirements. NIST’s AI RMF 1.0 calls for context-sensitive risk treatment, attention to robustness and reliability, testing and monitoring after deployment, and possible human intervention when an AI system cannot detect or correct errors. It says risks that could lead to serious injury or death warrant the most urgent prioritization and thorough management.
6. Reassess when the system or work changes
Review the risk assessment when software or a model, task, tooling, layout, load, operating speed, access pattern, or maintenance procedure changes. Track incidents and near misses, define who can stop and recover the system, and ensure operators and maintainers understand the procedures for their work. This ongoing review follows the lifecycle and system-level guidance; it is not a universal checklist attributed to one standard.
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How common safeguard approaches differ
Compare each option against the hazards it controls, whether it prevents contact or limits it, its detection and stopping performance, coverage of the work envelope and tooling, response to failures, access needs, integration, and validation evidence. The right choice depends on the specific people, environment, and jurisdiction in scope.
| Approach | How it reduces exposure | Key design and validation question |
|---|---|---|
| Physical guarding and interlocks | A barrier separates people from the hazard; an interlock can stop or inhibit operation when access is opened. | Does the barrier protect the relevant access routes while still allowing required setup, work, and recovery to be done safely? |
| Presence sensing | Detects a person or body part entering a protected zone and initiates a protective response. | Is the sensing equipment selected and positioned for the hazard and the system’s stopping behavior, with the necessary area covered? |
| Speed-and-separation monitoring (SSM) | Constrains robot speed while maintaining sufficient distance from a person; NIST describes the method as intended to prevent contact. | Does the system maintain the required separation as people and the robot move, and has its performance been evaluated for the application? NIST’s testbed publication provides context for evaluating SSM. |
| Power-and-force limiting (PFL) | Limits contact forces or pressures and can use protective stopping when limits are exceeded. | Are contact conditions and injury-related measures evaluated for the specific robot, tooling, and task? NIST’s 2012 measurement paper discusses measurement methods and injury metrics; it is technical background, not a substitute for current applicable standards or a site evaluation. |
| AI monitoring and human intervention | Supports risk management for AI behavior through testing, robustness work, deployment information, monitoring, and a plan for intervention when errors or unexpected conditions arise. | What conditions require intervention, who is responsible, and how can the system be stopped or recovered safely? This AI-risk layer does not replace physical safeguards or machine-safety requirements. |
What U.S. OSHA guidance does—and does not—say
OSHA states, “There are currently no specific OSHA standards for the robotics industry.” That is about robotics-specific OSHA standards; it does not mean that no workplace requirements apply. OSHA lists other applicable requirements and national consensus standards related to robotics, while noting that consensus standards are guidance from their originating organizations, not OSHA regulations. Do not assume an ISO standard is automatically law in every U.S. workplace. Determine the federal, state-plan, industry, and application-specific requirements that apply, and verify current editions and jurisdictional adoption before treating a standard as legally controlling. OSHA’s robotics standards page includes dated references.
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Conclusion
Safer physical AI depends on a defined application, a system-level hazard assessment, safeguards matched to the risks, and evidence that the integrated system performs as intended. Treat AI risk management as an additional lifecycle layer, not as a substitute for robot and application safety requirements.
Quick Recap
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