论文标题
使用试听和触觉的强大机器人倾泻
Robust Robotic Pouring using Audition and Haptics
论文作者
论文摘要
对液体高度的强大而准确的估计是为服务机器人倾注任务的重要组成部分。但是，基于视觉的方法通常在封闭的条件下失败，而基于音频的方法在嘈杂的环境中无法正常工作。相反，我们提出了一个多模式浇注网络（MP-NET），该网络能够通过对试听和触觉输入进行调节来稳健地预测液体高度。 MP-NET经过自收集的多模式浇注数据集的训练。该数据集包含300个机器人浇注录音，并具有三种类型的目标容器的音频和力/扭矩测量。我们还通过插入机器人噪声来增加音频数据。我们在收集的数据集和各种机器人实验上评估了MP-NET。网络训练结果和机器人实验都表明，MP-NET对噪声和任务和环境的变化都有强大的态度。此外，我们进一步结合了预测的高度和强制数据，以估计目标容器的形状。
Robust and accurate estimation of liquid height lies as an essential part of pouring tasks for service robots. However, vision-based methods often fail in occluded conditions while audio-based methods cannot work well in a noisy environment. We instead propose a multimodal pouring network (MP-Net) that is able to robustly predict liquid height by conditioning on both audition and haptics input. MP-Net is trained on a self-collected multimodal pouring dataset. This dataset contains 300 robot pouring recordings with audio and force/torque measurements for three types of target containers. We also augment the audio data by inserting robot noise. We evaluated MP-Net on our collected dataset and a wide variety of robot experiments. Both network training results and robot experiments demonstrate that MP-Net is robust against noise and changes to the task and environment. Moreover, we further combine the predicted height and force data to estimate the shape of the target container.
