In recent years, the effective and safe collaboration between humans and
machines has gained significant importance, particularly in the Industry 4.0
scenario. A critical prerequisite for realizing this collaborative paradigm is
precisely understanding the robot's 3D pose within its environment. Therefore,
in this paper, we introduce a novel vision-based system leveraging depth data
to accurately establish the 3D locations of robotic joints. Specifically, we
prove the ability of the proposed system to enhance its current pose estimation
accuracy by jointly learning to forecast future poses. Indeed, we introduce the
concept of Pose Nowcasting, denoting the capability of a system to exploit the
learned knowledge of the future to improve the estimation of the present. The
experimental evaluation is conducted on two different datasets, providing
state-of-the-art and real-time performance and confirming the validity of the
proposed method on both the robotic and human scenarios.

近年来，人与机器之间的有效和安全协作在第四工业革命场景中变得极为重要。而实现这种协作范式的一个关键前提就是准确理解机器人在其环境中的三维姿态。因此，在本文中，我们介绍了一种利用深度数据来准确确定机器人关节的三维位置的新型基于视觉的系统。具体而言，我们证明了所提出的系统通过共同学习预测未来姿态的能力，能够提高当前姿态估计的准确性。事实上，我们引入了姿态预测的概念，即系统利用对未来的学习知识来改善对当前姿态的估计能力。我们在两个不同的数据集上进行了实验评估，展示出最先进且实时的性能，并验证了所提出方法在机器人和人类场景下的有效性。