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 ISAAC Sim and Gazebo will allow us to integrate and evaluate different robotic hands for grasping a variety of everyday objects. We will evaluate grasp performance using different metrics (e.g., object-category-wise, affordance-wise, etc.), and finally, test the sim2real gap when transferring such approaches from popular simulators to real robots. 

TIAGo++ and Rollin Justin

The student will have the chance to work with different robotic hands (Justin hand, PAL TIAGo hands, Robotiq gripper, Panda gripper, etc.) and is expected to transfer the results to at least two robots (Rollin’ Justin at DLR and TIAGo++ at TU Darmstadt). The results of this thesis are intended to be made public (both the data and the benchmarking framework) for the benefit of the robotics community. 

As this thesis is offered in collaboration with the DLR institute of Robotics and Mechatronics in Oberpfaffenhofen near Munich, the student is expected to work in DLR for a period of 8-months for the thesis. On-site work at the premises of DLR can be expected but not guaranteed due to COVID-19 restrictions. A large part of the project can be carried out remotely. 

Highly motivated students can apply by sending  an e-mail expressing their interest to daniel.leidner@dlr.de and georgia.chalvatzaki@tu-darmstadt.de

Please attach your CV and your transcripts.

References:

[1] Collins, Jack, Shelvin Chand, Anthony Vanderkop, and David Howard. “A Review of Physics Simulators for Robotic Applications.” IEEE Access (2021).

[2] Bekiroglu, Y., Marturi, N., Roa, M. A., Adjigble, K. J. M., Pardi, T., Grimm, C., … & Stolkin, R. (2019). Benchmarking protocol for grasp planning algorithms. IEEE Robotics and Automation Letters, 5(2), 315-322.

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