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Precise pick-and-place is essential in robotic applications. To this end, wedefine a novel exact training method and an iterative inference method thatimprove pick-and-place precision with Transporter Networks. We conduct a largescale experiment on 8 simulated tasks. A systematic analysis shows, that theproposed modifications have a significant positive effect on model performance.Considering picking and placing independently, our methods achieve up to 60%lower rotation and translation errors than baselines. For the wholepick-and-place process we observe 50% lower rotation errors for most tasks withslight improvements in terms of translation errors. Furthermore, we proposearchitectural changes that retain model performance and reduce computationalcosts and time. We validate our methods with an interactive teaching procedureon real hardware. Supplementary material will be made available at:https://gergely-soti.github.io/p

Train What You Know -- Precise Pick-and-Place with Transporter Networks