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- W3132496303 abstract "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." @default.
- W3132496303 created "2021-03-01" @default.
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- W3132496303 date "2020-10-24" @default.
- W3132496303 modified "2023-09-30" @default.
- W3132496303 title "Robust Robotic Pouring using Audition and Haptics" @default.
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- W3132496303 doi "https://doi.org/10.1109/iros45743.2020.9340859" @default.
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