Physical AI's Promise Meets Reality: A Practical Strategy for Robot Companies

The hottest topic in the robotics industry today is undoubtedly "Physical AI." Just as ChatGPT broke down language barriers in the digital world, Robot Foundation Models (RFM)—artificial intelligence combined with physical entities that can judge and act autonomously—are now poised to reshape the industrial landscape.

However, a significant technological and economic gap remains between rosy expectations and the harsh realities of industrial settings. While the data-driven "brain" has advanced dramatically, connecting it to actual muscles and nerves presents challenges of an entirely different dimension. Customers seeking automation always raise the same concern: "Can we deploy Physical AI in our factory right now?" We must answer this question.

The Implications of Atlas's Roadmap

Hyundai Motor Group recently unveiled an ambitious industrial application roadmap utilizing Boston Dynamics' humanoid robot "Atlas." The plan centers on deploying the robot for parts sorting and sequencing at the Georgia Metaplant (HMGMA) in the United States by 2028, followed by parts assembly on the trim line by 2030.

The key point here is the difference in technical difficulty between 2028 and 2030. Sorting and sequencing occur on standardized pallets, but the trim line is the least automated process requiring the most sophisticated "tacit knowledge" from human workers.

Achieving this requires three evolutionary stages. Stage 1 is accurately identifying numerous objects in complex environments (Vision). Stage 2 is precise positioning and movement within a 0.1mm error margin (Motion). Stage 3 is manipulating objects flexibly and dexterously like humans using robot hands (Manipulation).

This raises a fundamental question. Giant corporations like Hyundai and Tesla can invest trillions of won in capital to design long-term goals three to four years out. But for most small and medium-sized AI and robotics companies lacking the massive capital and workforce to quench their "data thirst," an "all-in" strategy centered on VLA could be extremely risky.

Becoming consumed solely by training large models without immediate results, or focusing exclusively on low-probability robot hand manufacturing, risks technological isolation. What we need now is a "smart detour strategy" that captures practical benefits rather than blindly chasing large corporations. Small and medium robot companies especially need optimal path-setting for "tangible" short-to-medium-term results.

AI-Enabled Precision Control: The Emergence of 'System 0'

The "Helix 02" architecture recently announced by Silicon Valley's Figure AI demonstrates a significant paradigm shift. Until now, robot balance and stability relied on classical control algorithms written in over 100,000 lines of manual C++ code. But Figure AI boldly deleted this and added a neural network-based "System 0" layer with 10 million parameters.

This implements unconscious reactions that bypass cognitive judgment through AI—the core of physical resilience that ensures work continuity even under external shocks or on irregular surfaces. While System 2 handles logical reasoning and System 1 coordinates vision-motor functions, System 0 manages physical stability and contact dynamics (the "Last 0.1mm") in high-speed loops of 1,000 cycles per second.

When a robot walks while carrying a heavy tray, AI "intuitively" corrects minute changes in the center of gravity. The expansion of AI into precision control signals a paradigm shift: precision itself is the core of robot automation.

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