RNN controller trained on human demos achieves 4ml pouring error on unseen containers for water and comparable accuracy for oil and syrup.
Accurate Pouring with an Autonomous Robot Using an RGB-D Camera
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abstract
Robotic assistants in a home environment are expected to perform various complex tasks for their users. One particularly challenging task is pouring drinks into cups, which for successful completion, requires the detection and tracking of the liquid level during a pour to determine when to stop. In this paper, we present a novel approach to autonomous pouring that tracks the liquid level using an RGB-D camera and adapts the rate of pouring based on the liquid level feedback. We thoroughly evaluate our system on various types of liquids and under different conditions, conducting over 250 pours with a PR2 robot. The results demonstrate that our approach is able to pour liquids to a target height with an accuracy of a few millimeters.
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cs.RO 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Accurate Robotic Pouring for Serving Drinks
RNN controller trained on human demos achieves 4ml pouring error on unseen containers for water and comparable accuracy for oil and syrup.