Loading of shipping containers for dairy products often includes a press-fit task, which involves manually stacking milk cartons in a container without using pallets or packaging. Automating this task with a mobile manipulator can reduce worker strain, and also enhance the efficiency and safety of the container loading process. This paper proposes an approach called Adaptive Compliant Control with Integrated Failure Recovery (ACCIFR), which enables a mobile manipulator to reliably perform the press-fit task. We base the approach on a demonstration learning-based compliant control framework, such that we integrate a monitoring and failure recovery mechanism for successful task execution. Concretely, we monitor the execution through distance and force feedback, detect collisions while the robot is performing the press-fit task, and use wrench measurements to classify the direction of collision; this information informs the subsequent recovery process. We evaluate the method on a miniature container setup, considering variations in the (i) starting position of the end effector, (ii) goal configuration, and (iii) object grasping position. The results demonstrate that the proposed approach outperforms the baseline demonstration-based learning framework regarding adaptability to environmental variations and the ability to recover from collision failures, making it a promising solution for practical press-fit applications.

翻译：乳制品集装箱的装载工作通常包含压配任务，即在不使用托盘或包装的情况下，将牛奶纸盒手动堆叠到集装箱中。使用移动机械臂自动化此任务可减轻工人劳动强度，同时提升集装箱装载过程的效率与安全性。本文提出一种名为"自适应柔顺控制与集成故障恢复"（ACCIFR）的方法，使移动机械臂能够可靠地执行压配任务。该方法基于示范学习式的柔顺控制框架，通过集成监测与故障恢复机制确保任务成功执行。具体而言，我们利用距离与力反馈监测执行过程，检测机器人执行压配任务时发生的碰撞，并通过力矩测量对碰撞方向进行分类；这些信息将指导后续的恢复流程。我们在一套微型集装箱实验装置上评估该方法，考虑了（i）末端执行器起始位置、（ii）目标构型以及（iii）物体抓取位置的变化。结果表明，所提方法在环境适应性及碰撞故障恢复能力方面均优于基线示范学习框架，使其成为实际压配任务中极具前景的解决方案。