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arXiv · 2511.17774

Learning Diffusion Policies for Robotic Manipulation of Timber Joinery under Fabrication Uncertainty

Salma Mozaffari·Daniel Ruan·William van den Bogert·Nima Fazeli·Sigrid Adriaenssens·Arash Adel
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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 under minimal geometric clearance. 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-clearance 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 joint clearance. The best-performing policy achieved 100% success under nominal conditions and 75% average success under uncertainty. These results suggest that diffusion policies can improve robustness to fabrication-induced misalignment, representing a step toward reliable robotic assembly in construction under tight tolerances.

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Salma Mozaffari, Daniel Ruan, William van den Bogert, Nima Fazeli, Sigrid Adriaenssens, Arash Adel. 2025-11-21. Learning Diffusion Policies for Robotic Manipulation of Timber Joinery under Fabrication Uncertainty. https://arxiv.org/abs/2511.17774

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