利用人类专业知识实现工业化建造中的高精度机器人装配：一种样本高效的安装者在环交互式强化学习框架
Harnessing human expertise for high-precision robotic assembly in industrialized construction: A sample-efficient installer-in-the-loop interactive reinforcement learning framework
- McGill University（麦吉尔大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对工业化建造中机器人装配的毫米级精度挑战，提出一种安装者在环的交互式强化学习框架，利用离线演示、稀疏接管和终端奖励，在2毫米间隙下实现100%自主就位，仅需12-15分钟监督。
AI中文摘要：
工业化建造对预制窗单元等模块化组件的机器人装配提出了严格的精度要求。在公差关键的操作中，核心瓶颈不仅在于机械间隙，还在于如何在稀疏的验收反馈、接触变异性和毫米级约束下，将安装者的隐性专业知识转化为数据高效的自主性。我们提出了一种安装者在环交互式强化学习框架，该框架通过离线遥操作演示、接触失败边界处的稀疏事件驱动二元接管以及与验收对齐的终端奖励来获取专业知识，并在统一模式下记录，以实现可追溯的离线到在线适应。基于Q分块和Flow Q-Learning构建的时间抽象动作序列策略，能够在稀疏终端奖励下捕获多模态恢复操作，而一个不更新的预热阶段则稳定了离线到在线的过渡。该框架在MuJoCo中进行了评估，覆盖了从吸盘获取到间隙受限就位的整个工作流程，并采用了结构化分阶段和端到端随机放置。在定义的应力测试条件下，即每侧2毫米间隙、有界位姿扰动和摩擦随机化，该流程在3.0小时的在线训练中，通过12至15分钟的累计安装者监督，实现了100%的自主就位，并在两个实验中分别在大约0.5小时和1.5小时内达到了95%的成功率里程碑。我们还报告了墙钟适应时间、累计接管分钟数、干预率衰减和分阶段失败归因，以为监督预算提供信息。消融研究分离了时间抽象、安装者干预和预热值校准的互补贡献。
英文摘要：
Industrialized construction imposes stringent precision requirements on robotic assembly of modular components such as prefabricated window units. In tolerance-critical operations, the central bottleneck is not only mechanical clearance but also converting tacit installer expertise into data-efficient autonomy under sparse acceptance feedback, contact variability, and millimeter-scale constraints. We present an installer-in-the-loop interactive reinforcement learning framework that acquires expertise through offline teleoperated demonstrations, sparse event-driven binary takeovers at contact-failure boundaries, and acceptance-aligned terminal rewards, logged under a unified schema for traceable offline-to-online adaptation. A temporally abstract action-sequence policy built on Q-chunking with Flow Q-Learning captures multimodal recovery maneuvers under sparse terminal rewards, while a non-updating warm-start phase stabilizes the offline-to-online transition. The framework is evaluated in MuJoCo across the workflow from suction acquisition through clearance-limited seating, under structured staging and end-to-end randomized placement. Within a defined stress-test regime with 2 mm per-side clearance, bounded pose perturbations, and friction randomization, the pipeline attains 100\% autonomous seating with 12--15 min of cumulative installer supervision over 3.0 h of online training, and reaches the 95\% success milestone in approximately 0.5 h and 1.5 h in the two experiments. We also report wall-clock adaptation time, cumulative takeover minutes, intervention-rate decay, and stage-wise failure attribution to inform supervision budgeting. Ablations isolate the complementary contributions of temporal abstraction, installer intervention, and warm-start value calibration.
