Contact-Rich Robotic Manipulation in Construction via Zero-Shot Learning: A Diffusion Policy-Guided Adaptive Control
Researchers presented a framework that pairs diffusion policies with adaptive control to enable zero-shot robotic assembly in construction settings. The approach targets contact-rich tasks where robots must handle tight tolerances, fabrication variances, and unpredictable contact forces without task-specific training data.
The method directly addresses persistent limits in automated construction processes that rely on physical interaction between components. By integrating learned diffusion-based action generation with real-time adaptive adjustments, the framework aims to improve consistency in assembly operations that current robotic systems often cannot complete reliably.
The work focuses exclusively on manipulation challenges within structured construction environments and does not report deployment metrics or comparisons with existing control methods.