用扩散模型+自适应控制实现建筑机器人零样本接触操作，成功率超90%。
Contact-Rich Robotic Manipulation in Construction via Zero-Shot Learning: A Diffusion Policy-Guided Adaptive Control
- 扩散政策结合自适应控制器在线修正动作，补偿未知接触力。
- 单任务成功率100%，多阶段桁架装配成功率90%-100%，力更稳定。
- 无需真实数据训练，可直接用于复杂建筑场景的力感知操作。
建筑机器人与自动化有望提升效率、缓解人力短缺并降低工人劳动强度。然而，在公差严苛、制造误差和接触动态不确定条件下，可靠的接触丰富型机器人装配仍具挑战。本文提出一种框架，将基于仿真生成的姿态与力/力矩数据训练的扩散策略，与受L1启发的自适应控制器结合，实时校正策略预测动作以补偿未建模的接触动态。在木结构接合、管道对接及全尺寸桁架分步装配任务中进行基准测试，该框架在单任务装配中达成100%成功率，在多步骤桁架子任务中成功率达90%-100%，且接触力更低、更稳定。该方法实现了力感知接触丰富装配的零样本仿真到现实迁移，减少了昂贵且耗时的真实数据采集，推动了多阶段装配的可扩展、鲁棒自动化，为建筑领域更多接触密集型操作提供了可行方案。
原文摘要 · Abstract (English)
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.
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