The process of industrial box-packing, which involves the accurate placement of multiple objects, requires high-accuracy positioning and sequential actions. When a robot is tasked with placing an object at a specific location with high accuracy, it is important not only to have information about the location of the object to be placed, but also the posture of the object grasped by the robotic hand. Often, industrial box-packing requires the sequential placement of identically shaped objects into a single box. The robot's action should be determined by the same learned model. In factories, new kinds of products often appear and there is a need for a model that can easily adapt to them. Therefore, it should be easy to collect data to train the model. In this study, we designed a robotic system to automate real-world industrial tasks, employing a vision-based learning control model. We propose in-hand-view-sensitive Newtonian variational autoencoder (ihVS-NVAE), which employs an RGB camera to obtain in-hand postures of objects. We demonstrate that our model, trained for a single object-placement task, can handle sequential tasks without additional training. To evaluate efficacy of the proposed model, we employed a real robot to perform sequential industrial box-packing of multiple objects. Results showed that the proposed model achieved a 100% success rate in industrial box-packing tasks, thereby outperforming the state-of-the-art and conventional approaches, underscoring its superior effectiveness and potential in industrial tasks.

翻译：工业装箱过程涉及多个物体的精确放置，需要高精度定位与顺序操作。当机器人需以高精度将物体置于特定位置时，不仅需要获取待放置物体的位置信息，还需掌握机械手所抓取物体的姿态。工业装箱通常要求将形状相同的物体依次放入同一箱体，且机器人的动作需由同一学习模型决定。工厂中常出现新型产品，亟需能灵活适应新产品的模型，因此应便于收集训练数据。本研究设计了一套基于视觉学习控制模型的机器人系统，以实现真实工业任务的自动化。我们提出了一种面向手内姿态感知的牛顿变分自编码器（ihVS-NVAE），通过RGB相机获取物体在机械手内的姿态。实验证明，针对单个物体放置任务训练的模型，无需额外训练即可处理顺序任务。为评估模型有效性，我们采用真实机器人执行多物体工业装箱顺序任务。结果表明，所提模型在工业装箱任务中实现了100%的成功率，优于现有最先进方法和传统方法，凸显了其在工业任务中的卓越效能与应用潜力。