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 TRO39(5), 3929–3945 (2023)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.