论文标题
Top-1 Corsmal Challenge 2020提交：使用人类机器人移交的多模式观察来填充质量估计
Top-1 CORSMAL Challenge 2020 Submission: Filling Mass Estimation Using Multi-modal Observations of Human-robot Handovers
论文作者
论文摘要
人类机器人对象移交是人类机器人协作未来的关键技能。 Corsmal 2020挑战的重点是此问题的感知部分：机器人需要估计人类持有的容器的填充质量。尽管在单独处理图像处理和音频处理中有强大的方法，但是回答此类问题需要将多个传感器的数据一起处理。容器的外观，填充声和深度数据提供了必不可少的信息。我们提出了一种多模式方法，以预测填充质量的三个关键指标：填充类型，填充水平和容器容量。然后将这些指标合并以估计容器的填充质量。我们的方法在对公共和私人子集的Corsmal 2020挑战的所有提交中获得了TOP-1总体表现，同时没有表现出过度拟合的证据。我们的源代码公开可用：https：//github.com/v-iashin/corsmal
Human-robot object handover is a key skill for the future of human-robot collaboration. CORSMAL 2020 Challenge focuses on the perception part of this problem: the robot needs to estimate the filling mass of a container held by a human. Although there are powerful methods in image processing and audio processing individually, answering such a problem requires processing data from multiple sensors together. The appearance of the container, the sound of the filling, and the depth data provide essential information. We propose a multi-modal method to predict three key indicators of the filling mass: filling type, filling level, and container capacity. These indicators are then combined to estimate the filling mass of a container. Our method obtained Top-1 overall performance among all submissions to CORSMAL 2020 Challenge on both public and private subsets while showing no evidence of overfitting. Our source code is publicly available: https://github.com/v-iashin/CORSMAL
