A contact microphone on a robot tray learns a velocity-dependent friction constraint that reduces object displacement during fast non-prehensile transport by an average of 86% in physical experiments.
Critically fast pick-and-place with suction cups
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abstract
Fast robotics pick-and-place with suction cups is a crucial component in the current development of automation in logistics (factory lines, e-commerce, etc.). By "critically fast" we mean the fastest possible movement for transporting an object such that it does not slip or fall from the suction cup. The main difficulties are: (i) handling the contact between the suction cup and the object, which fundamentally involves kinodynamic constraints; and (ii) doing so at a low computational cost, typically a few hundreds of milliseconds. To address these difficulties, we propose (a) a model for suction cup contacts, (b) a procedure to identify the contact stability constraint based on that model, and (c) a pipeline to parameterize, in a time-optimal manner, arbitrary geometric paths under the identified contact stability constraint. We experimentally validate the proposed pipeline on a physical robot system: the cycle time for a typical pick-and-place task was less than 5 seconds, planning and execution times included. The full pipeline is released as open-source for the robotics community.
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Hearing the Slide: Acoustic-Guided Constraint Learning for Fast Non-Prehensile Transport
A contact microphone on a robot tray learns a velocity-dependent friction constraint that reduces object displacement during fast non-prehensile transport by an average of 86% in physical experiments.