Industrial robots are increasingly deployed in contact-rich construction and manufacturing tasks that involve uncertainty and long-horizon execution. While learning-based visuomotor policies offer a promising alternative to open-loop control, their deployment on industrial platforms is challenged by a large observation-execution gap caused by sensing, inference, and control latency. This gap is significantly greater than on low-latency research robots due to high-level interfaces and slower closed-loop dynamics, making execution timing a critical system-level issue. This paper presents a latency-aware framework for deploying and evaluating visuomotor policies on industrial robotic arms under realistic timing constraints. The framework integrates calibrated multimodal sensing, temporally consistent synchronization, a unified communication pipeline, and a teleoperation interface for demonstration collection. Within this framework, we introduce a latency-aware execution strategy that schedules finite-horizon, policy-predicted action sequences based on temporal feasibility, enabling asynchronous inference and execution without modifying policy architectures or training. We evaluate the framework on a contact-rich industrial assembly task while systematically varying inference latency. Using identical policies and sensing pipelines, we compare latency-aware execution with blocking and naive asynchronous baselines. Results show that latency-aware execution maintains smooth motion, compliant contact behavior, and consistent task progression across a wide range of latencies while reducing idle time and avoiding instability observed in baseline methods. These findings highlight the importance of explicitly handling latency for reliable closed-loop deployment of visuomotor policies on industrial robots.

翻译：工业机器人正越来越多地部署于涉及不确定性和长时程执行的接触密集型建筑与制造任务中。虽然基于学习的视觉运动策略为开环控制提供了一种有前景的替代方案，但其在工业平台上的部署面临着由感知、推理和控制延迟所导致的巨大观测-执行间隙的挑战。由于高级接口和较慢的闭环动力学特性，该间隙显著大于低延迟研究机器人，使得执行时序成为一个关键的系统级问题。本文提出了一种延迟感知框架，用于在现实时序约束下在工业机械臂上部署和评估视觉运动策略。该框架集成了标定后的多模态感知、时间一致的同步机制、统一的通信管道以及用于演示数据采集的遥操作接口。在此框架内，我们引入了一种延迟感知执行策略，该策略基于时序可行性来调度由策略预测的有限时域动作序列，从而在不修改策略架构或训练过程的情况下实现异步推理与执行。我们在一个接触密集型工业装配任务上评估该框架，同时系统性地改变推理延迟。使用相同的策略和感知管道，我们将延迟感知执行与阻塞式和朴素的异步基线方法进行比较。结果表明，延迟感知执行在广泛的延迟范围内能保持平滑运动、顺应性接触行为以及一致的任务推进，同时减少了空闲时间并避免了基线方法中观察到的不稳定性。这些发现突显了显式处理延迟对于在工业机器人上可靠地闭环部署视觉运动策略的重要性。