{"ID":2838792,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.17774","arxiv_id":"2511.17774","title":"Contact-Rich Robotic Assembly in Construction via Diffusion Policy Learning","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.17774","url_pdf":"https://arxiv.org/pdf/2511.17774v3","authors":"[\"Salma Mozaffari\",\"Daniel Ruan\",\"William van den Bogert\",\"Nima Fazeli\",\"Sigrid Adriaenssens\",\"Arash Adel\"]","published":"2025-11-21T20:43:46Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Diffusion Model\"]","has_code":false}