Fabrication uncertainties, such as tolerance accumulation and material imperfections, pose a significant challenge to contact-rich robotic manipulation in construction by hindering precise and robust assembly. In this paper, we investigate the performance and robustness of diffusion policy learning for contact-rich assembly at the construction scale, using a tight-fitting timber mortise and tenon joint as a case study. A two-phase experimental study is conducted: first, to evaluate baseline policy performance and applicability; second, to assess policy robustness under fabrication-induced uncertainties modeled as randomized perturbations to the mortise position. The diffusion policy is trained on teleoperated demonstrations using an industrial robotic arm conditioned on end-effector pose and force/torque feedback. The best-performing policy achieved a total average success rate of 75% under perturbations up to 10 mm, including 100% success in unperturbed cases. The results demonstrate the potential of sensory-motor diffusion policies to enable high-precision contact-rich manipulation on large-scale industrial robotic arms, reducing reliance on skilled manual intervention. This work advances robotic construction under uncertainty and provides practical insights for deploying learning-based control in real-world Architectural, Engineering, and Construction applications.

翻译：制造不确定性（如公差累积与材料缺陷）会阻碍精确且鲁棒的装配过程，从而对建筑领域中接触密集的机器人操控构成重大挑战。本文以紧密配合的木材榫卯接头为案例，研究了扩散策略学习在建筑尺度下接触密集装配任务中的性能与鲁棒性。研究进行了两阶段实验：首先评估基线策略的性能与适用性；其次评估在制造不确定性（建模为榫孔位置的随机扰动）下策略的鲁棒性。扩散策略基于示教操作演示进行训练，使用工业机械臂，并以末端执行器位姿及力/力矩反馈为条件。在扰动高达10毫米的情况下，表现最佳的策略实现了75%的总平均成功率，其中无扰动情况下成功率达100%。结果表明，感觉运动扩散策略有望在大型工业机械臂上实现高精度、接触密集的操控，从而减少对熟练人工干预的依赖。这项工作推进了不确定性下的机器人建造技术，并为在实际建筑、工程与施工应用中部署基于学习的控制提供了实用见解。