A learned pick-optimization model, trained on gradients derived from a pick-success predictor, reduces missed-pick failures by about 19% in a 2-million-pick warehouse robotics A/B test.
IEEE TRO30(2), 289–309 (2014)
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Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction
A learned pick-optimization model, trained on gradients derived from a pick-success predictor, reduces missed-pick failures by about 19% in a 2-million-pick warehouse robotics A/B test.