Robot brain builders—those fusing robotics with AI—are breaking through previous technical constraints and entering a new phase. The movement to break away from the GPT-2-equivalent inference model framework traditionally used in many robot controls and realize higher-level intelligence in the physical world is accelerating.
This trend is not merely an expansion of language models. Foundation models for robots to perceive their environment and perform physical operations are evolving. Rather than being limited to specific product releases, the intelligence hierarchy in the robot's action-decision process is designed to execute more complex inferences in real time.
In the former GPT-2 era, a robot's decision-making capacity was limited and optimized for simple repetitive motions. Today, however, end-to-end learning paradigms that integrate vision, touch, and motion control are becoming widespread. This enables flexible decision-making even in uncertain environments.
Robot brain builders are strengthening the process of integrating higher-order models into physical hardware. Moving forward, the focus will shift to infrastructure development to ensure that intelligent robots with general-purpose cognitive capabilities can be widely put into practical use, both in industrial and domestic settings.