Thesis

Master Thesis: Discovering neural parts in objects with invertible NNs for robot grasping

In this thesis, we will investigate the use of 3D primitive representations in objects using Invertible Neural Networks (INNs). Through INNs we can learn the implicit surface function of the objects and their mesh. Apart from extracting the object’s shape, we can parse the object into semantically interpretable parts. In our work our main focusContinue reading “Master Thesis: Discovering neural parts in objects with invertible NNs for robot grasping”

Master Thesis/Job@DLR: Cross-platform Benchmark of Robot Grasp Planning

Grasp planning is one of the most challenging tasks in robot manipulation. Apart from perception ambiguity, the grasp robustness and the successful execution rely heavily on the dynamics of the robotic hands. The student is expected to research and develop benchmarking environments and evaluation metrics for grasp planning. The development in simulation environments as ISAACContinue reading “Master Thesis/Job@DLR: Cross-platform Benchmark of Robot Grasp Planning”