Opinion: What happens when the robot is wrong?
Opinion: What happens when the robot is wrong?
This opinion piece by Gleb Tsipursky argues that as industrial robots accelerate with faster cycle times, manufacturers must test human escalation protocols for machine disagreements. The author proposes commissioning rehearsals involving false positives and conflicting instructions to verify safe decision rights without entering hazardous areas. Effective scorecards should measure challenge resolution times and evidence retention rather than only uptime or line speed.
Source: Robotics & Automation News · Read original article ↗
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Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
rehearsals
3
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tagout, emergency stops or task-based risk assessments. Workers should never have to enter a robot’s hazardous operating area to contest an automated recommendation. Instead, give operators a safe route to challenge the decision itself. I would include three rehearsals in commissioning. First, introduce a plausible false positive, such as a harmless part flagged by inspection software. The operator should be able to
Open source S7years
2,025
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d five million units in 2025, with more than 600,000 new installations that year. Increased capacity is real. So is the need to know how employees can challenge the system before a mistake spreads across a shift. The latest performance improvements make the issue more urgent. As Robotics & Automation News reported on October 20, ABB’s new IRB 1700 models promise up to 20 percent shorter cycle times , with precision a
Open source S3years
2,026
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rtant information. This is more than a theoretical governance exercise. NIST’s 2026 roadmap for AI and machine learning in smart manufacturing identifies reliable, explainable operation and the integration of complex industrial systems as central challenges. Its separate AI for Manufacturing research initiative explicitly includes metrics for human-AI teaming and methods to evaluate operator understanding of AI solut
Open source S10
- paper: not_reported
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Automatically extracted; no manual editorial approval recorded.
By Gleb Tsipursky , PhD, CEO, Disaster Avoidance Experts A new industrial robot can meet its cycle-time target and still fail its most important workplace test: what happens when an experienced operator believes the machine is wrong? That question deserves attention as manufacturers accelerate automation. The International Federation of Robotics reported in September that the global stock of industrial robots exceede
Open source S2
d five million units in 2025, with more than 600,000 new installations that year. Increased capacity is real. So is the need to know how employees can challenge the system before a mistake spreads across a shift. The latest performance improvements make the issue more urgent. As Robotics & Automation News reported on October 20, ABB’s new IRB 1700 models promise up to 20 percent shorter cycle times , with precision a
Open source S3
gets raised and resolved may be a sign of a healthy system, not an inconvenient failure. The numbers also offer a better way to evaluate training. Ask an operator to explain what the robot can determine, what data it cannot see, and what they should do when observations conflict. Give them a realistic scenario to practice. Watching a vendor demo is useful orientation; handling a disagreement safely and correctly is
Open source S12
tagout, emergency stops or task-based risk assessments. Workers should never have to enter a robot’s hazardous operating area to contest an automated recommendation. Instead, give operators a safe route to challenge the decision itself. I would include three rehearsals in commissioning. First, introduce a plausible false positive, such as a harmless part flagged by inspection software. The operator should be able to
Open source S7
rtant information. This is more than a theoretical governance exercise. NIST’s 2026 roadmap for AI and machine learning in smart manufacturing identifies reliable, explainable operation and the integration of complex industrial systems as central challenges. Its separate AI for Manufacturing research initiative explicitly includes metrics for human-AI teaming and methods to evaluate operator understanding of AI solut
Open source S10
Source：Robotics & Automation News · roboticsandautomationnews.com