Robots and AI Reach Sub‑0.1 mm Precision for Fusion Reactor Assembly

Researchers at the Hefei Institutes of Physical Science (HIPS) in China have demonstrated a robotics-based method to assemble and maintain components inside fusion reactors with sub‑0.1 millimeter precision. The team combined simple 2D vision with a force/torque sensor and artificial intelligence to solve the demanding "peg‑in‑hole" alignment tasks that will be required in future fusion power plants.

What the team did

The researchers trained industrial manipulators using deep reinforcement learning — a machine‑learning technique that lets robots learn skills through trial, error and feedback — to emulate human hand‑eye coordination. By pairing a 2D camera with force feedback, the system adapts to tight tolerances and delicate contact tasks in complex, hazardous environments while keeping humans out of harm's way.

Why it matters

Assembly operations with margins of error below one tenth of a millimeter are essential for the precise installation and maintenance of next‑generation fusion devices. Unlike nuclear fission, fusion produces primarily helium and heat and does not generate long‑lived, high‑level radioactive waste; making the mechanical supply chain and service operations safer and more reliable is a practical step toward eventual deployment.

Context and outlook

The HIPS work complements international efforts to accelerate fusion: governments are refining policy frameworks to support projects, and research groups — such as at the University of Wisconsin — are advancing magnet and confinement technologies. Timelines for commercial fusion vary widely, from roughly a decade to many decades, but innovations in robotics, sensors and control software remove real engineering barriers and reduce operational risk.

"Together, these innovations mark a significant step toward building intelligent, heavy‑duty robotic systems capable of carrying out complex and high‑risk maintenance tasks in future fusion power plants."

By improving precision, safety and automation, the HIPS approach helps bridge the gap between laboratory concepts and practical, maintainable fusion plants. Continued advances in AI, sensing and policy support will determine how quickly fusion becomes a reliable, low‑carbon energy option.