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

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.

Robotics (cs.RO)

arXiv:2511.17774 [cs.RO]

(or arXiv:2511.17774v4 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2511.17774 Focus to learn more arXiv-issued DOI via DataCite

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