Figure AI Robots at BMW: What Six Months of Factory Deployment Has Revealed | Taha Abbasi

Figure AI’s deployment of humanoid robots at BMW’s Spartanburg, South Carolina factory has been running for over six months, providing the first substantial real-world data on humanoid robots in automotive manufacturing. Taha Abbasi examines what the deployment has revealed about the future of factory automation.

What the Robots Are Actually Doing

Contrary to science fiction visions of robots assembling cars from scratch, Figure’s robots at BMW perform specific, well-defined tasks: moving materials between stations, loading and unloading parts from bins, and performing simple repetitive actions that are ergonomically challenging for human workers.

These aren’t glamorous tasks, but they’re economically significant. Material handling represents roughly 30-40% of labor hours in automotive assembly plants. As Taha Abbasi has previously covered, automating even a fraction of these tasks translates to meaningful cost reductions and allows human workers to focus on higher-value assembly and quality control.

Performance Metrics

While Figure hasn’t published detailed performance statistics, industry sources indicate the robots are achieving approximately 80-85% of human speed on assigned tasks, with reliability rates above 90% during operational periods. Taha Abbasi notes these numbers are impressive for first-generation deployment — early industrial robots in the 1960s took years to reach comparable reliability.

The key advantage isn’t speed per task but duration per shift. Figure’s robots operate 20+ hours per day versus 8-hour human shifts. Even at 80% speed, a robot that works 2.5x longer hours achieves more total output than a human worker on the same task.

What’s Working

Navigation in structured factory environments has exceeded expectations. The robots move confidently through defined pathways, avoid obstacles (including human workers), and handle minor environmental variations without intervention. The factory floor, with its defined lanes and predictable traffic patterns, is an easier navigation challenge than household or outdoor environments.

Object manipulation for standardized parts — bins, trays, and packaged components — works reliably. The robots can identify parts by vision, grip them appropriately, and place them accurately at target locations.

What’s Still Challenging

Handling non-standardized items remains difficult. Parts that are irregularly shaped, flexible, or arrive in unpredictable orientations cause errors. Taha Abbasi sees this as the key development frontier — manufacturing environments have far more variability than robotics demos typically show.

Recovery from errors also needs improvement. When a robot drops a part or encounters an unexpected situation, it typically pauses and requires human assistance. Autonomous error recovery — picking up a dropped item, finding an alternative path around an obstacle — is technically achievable but not yet reliable enough for production environments.

BMW’s Assessment

BMW has described the deployment as a “successful pilot” and indicated plans to expand the number of robots and the range of tasks. This language suggests the robots are meeting performance thresholds but aren’t yet at the scale where they’re fundamentally changing production economics.

Implications for Tesla Optimus

Figure’s BMW deployment provides a benchmark for Tesla’s Optimus program. As Taha Abbasi has analyzed, Tesla has the advantage of deploying Optimus in its own factories, enabling faster iteration. But Figure’s third-party deployment demonstrates that the market for factory humanoid robots extends beyond self-consumption — automakers will buy these robots from whoever delivers the best product.

The humanoid robot race is on, and BMW’s factory floor is its first real proving ground.

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About the Author: Taha Abbasi is a technology executive, CTO, and applied frontier tech builder. Read more on Grokpedia | YouTube: The Brown Cowboy | tahaabbasi.com