基于世界模型的控制：使用 NewtonianVAE 实现多物体工业装箱
World-Model-Based Control for Industrial box-packing of Multiple Objects using NewtonianVAE
AI总结：
本文提出手内视角敏感的牛顿变分自编码器（ihVS-NVAE），利用 RGB 相机获取物体手内姿态，实现无需额外训练的多物体顺序工业装箱，并在真实机器人上达到 100% 成功率。
摘要：
工业装箱过程涉及多个物体的精确放置，要求高精度定位和顺序动作。当机器人被要求以高精度将物体放置在特定位置时，不仅需要了解待放置物体的位置信息，还需要掌握机器人手部抓取物体的姿态。通常，工业装箱需要将形状相同的物体依次放入同一个箱子中。机器人的动作应由同一个已学习模型决定。在工厂中，新产品种类经常出现，因此需要一种能够轻松适应新产品的模型。因此，收集用于训练模型的数据应当简便易行。在本研究中，我们设计了一个机器人系统，采用基于视觉的学习控制模型来自动化真实世界的工业任务。我们提出了手内视角敏感的牛顿变分自编码器（ihVS-NVAE），该模型使用 RGB 相机获取物体的手内姿态。我们证明，针对单一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。为了评估所提模型的有效性，我们使用真实机器人执行多物体的顺序工业装箱。结果表明，所提模型在工业装箱任务中达到了 100% 的成功率，从而优于最先进的方法和传统方法，突显了其在工业任务中的卓越有效性和潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
