Humans have the ability to pour various media, both liquid and granular, to desired ends in various containers. We do this by using multiple senses simultaneously in a constant feedback loop to complete a pouring task. Combining multiple sensing modalities, similar to humans, could aid in robotic pouring control outside of a structured or industrial setting. We present a multi-sensory pouring dataset consisting of human pouring demonstrations of various granular media, coupled with two multi-sensory networks that estimate pouring rate and pouring average height. For both pouring metrics, a combined input of audio and visual data provides a lower median error than either the audio network or visual network. The multi-sensory network achieves a median error of 6.4 mm for average height estimation and 0.06 N/s for pouring rate estimation.
|Original language||English (US)|
|Title of host publication||2022 IEEE International Conference on Robotics and Automation, ICRA 2022|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - 2022|
|Event||39th IEEE International Conference on Robotics and Automation, ICRA 2022 - Philadelphia, United States|
Duration: May 23 2022 → May 27 2022
|Name||2022 International Conference on Robotics and Automation (ICRA)|
|Conference||39th IEEE International Conference on Robotics and Automation, ICRA 2022|
|Period||5/23/22 → 5/27/22|
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