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Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation
In particular, we focus on training with multi-object data instead of singulated objects, combined with a well-chosen augmentation scheme.
Supervised Training of Dense Object Nets using Optimal Descriptors for Industrial Robotic Applications
In this paper we show that given a 3D model of an object, we can generate its descriptor space image, which allows for supervised training of DONs.
Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks
Furthermore, to accomplish complex manipulation tasks, robots should be able to sequence several skills and adapt them to changing situations.
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