Precise pick-and-place is essential in robotic applications. To this end, we define a novel exact training method and an iterative inference method that improve pick-and-place precision with Transporter Networks. We conduct a large scale experiment on 8 simulated tasks. A systematic analysis shows, that the proposed 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 whole pick-and-place process we observe 50% lower rotation errors for most tasks with slight improvements in terms of translation errors. Furthermore, we propose architectural changes that retain model performance and reduce computational costs and time. We validate our methods with an interactive teaching procedure on real hardware. Supplementary material will be made available at: https://gergely-soti.github.io/p

翻译：精确拾取与放置在机器人应用中至关重要。为此，我们定义了一种新颖的精确训练方法和一种迭代推理方法，通过传输网络提升拾取与放置的精度。我们在8个模拟任务上进行了大规模实验。系统性分析表明，所提出的改进对模型性能具有显著的正面影响。若将拾取与放置独立考虑，我们的方法在旋转和平移误差上比基准方法降低了高达60%。对于整个拾取与放置过程，我们观察到大多数任务的旋转误差降低了50%，平移误差略有改善。此外，我们还提出了保留模型性能的同时降低计算成本与时间的架构改进。我们通过交互式教学程序在实际硬件上验证了这些方法。补充材料将发布于：https://gergely-soti.github.io/p