发表机构
Princeton University（普林斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出扩散策略与L1启发自适应控制器耦合的框架，实现建筑接触丰富型机器人装配的零样本仿真到真实迁移，在多类任务中取得高成功率，降低真实数据需求，推进多阶段装配自动化。
AI 中文摘要
建筑机器人与自动化技术为提升生产力、缓解劳动力短缺、减少工人从事高强度体力任务的暴露提供了可行手段。但在装配公差严格、制造精度不足、接触动力学不确定的情况下，可靠的接触丰富型机器人装配仍具挑战性。为解决该问题，本研究提出一种框架，将基于仿真生成的位姿、力/力矩数据训练的扩散策略，与受L1启发的自适应控制器耦合，该控制器可在线校正策略预测的动作，以补偿未建模的接触动力学。我们在木材榫接、管道装配及顺序全尺寸桁架装配任务中，将该框架与基线方法进行基准测试。其在单任务装配中达到100%成功率，在顺序桁架装配子任务中成功率为90%-100%，且接触力低于基线方法、稳定性更佳。通过实现力感知接触丰富型装配的零样本仿真到真实场景迁移，该框架减少了成本高昂且劳动密集的策略训练真实数据收集工作，推进了多阶段装配的可扩展、鲁棒自动化，为扩展至建筑领域更广泛的接触丰富型操作任务提供了动力。
英文摘要
Construction robotics and automation offer promising means of improving productivity, alleviating workforce shortages, and reducing workers' exposure to physically demanding tasks. However, reliable contact-rich robotic assembly remains challenging under tight tolerances, fabrication inaccuracies, and uncertain contact dynamics. To address this challenge, we present a framework coupling diffusion policies trained on simulation-generated pose and force/torque data with an L1-inspired adaptive controller that corrects policy-predicted actions online to compensate for unmodeled contact dynamics. We benchmark the framework against baselines in timber joinery, pipe fitting, and sequential full-scale truss assembly. It achieves 100% success on single-task assemblies and 90-100% success across sequential truss assembly subtasks, with lower, more stable contact forces than the baselines. By enabling zero-shot sim-to-real transfer for force-aware contact-rich assembly, the framework reduces costly, labor-intensive real-world data collection for policy training and advances scalable, robust automation of multistage assembly, motivating extension to broader contact-rich manipulation tasks in construction.
