Contact-Rich Robotic Assembly in Construction via Diffusion Policy Learning

TL;DR

This paper demonstrates that diffusion policy learning enables industrial robots to perform contact-rich assembly tasks with high precision and robustness despite fabrication uncertainties and positional errors.

Contribution

It introduces a novel application of diffusion policies trained via teleoperation to improve robustness in contact-rich construction assembly tasks.

Findings

Best policy achieved 100% success under nominal conditions.

Policy maintained 75% success rate under 10 mm positional perturbations.

Diffusion policies effectively compensate for misalignments in contact-rich tasks.

Abstract

Fabrication uncertainty arising from tolerance accumulation, material imperfection, and positioning errors remains a critical barrier to automated robotic assembly in construction, particularly for contact-rich manipulation tasks governed by friction and geometric constraints. This paper investigates the deployment of diffusion policy learning on construction-scale industrial robots to enable robust, high-precision assembly under such uncertainty, using tight-fitting mortise and tenon timber joinery as a representative case study. Sensory-motor diffusion policies are trained using teleoperated demonstrations collected from an industrial robotic workcell equipped with force/torque sensing. A two-phase experimental study evaluates baseline performance and robustness under randomized positional perturbations up to 10 mm, far exceeding the sub-millimeter joint clearance. The best-performing policy achieved 100% success under nominal conditions and 75% average success under uncertainty. These results provide initial evidence that diffusion policies compensate for misalignments through contact-aware control, representing a step toward robust robotic assembly in construction under tight tolerances.