Efficient End-to-End Detection of 6-DoF Grasps for Robotic Bin Picking
<|reference_start|>Efficient End-to-End Detection of 6-DoF Grasps for Robotic Bin Picking: Bin picking is an important building block for many robotic systems, in logistics, production or in household use-cases. In recent years, machine learning methods for the prediction of 6-DoF grasps on diverse and unknown objects have shown promising progress. However, existing approaches only consider a single ground truth grasp orientation at a grasp location during training and therefore can only predict limited grasp orientations which leads to a reduced number of feasible grasps in bin picking with restricted reachability. In this paper, we propose a novel approach for learning dense and diverse 6-DoF grasps for parallel-jaw grippers in robotic bin picking. We introduce a parameterized grasp distribution model based on Power-Spherical distributions that enables a training based on all possible ground truth samples. Thereby, we also consider the grasp uncertainty enhancing the model's robustness to noisy inputs. As a result, given a single top-down view depth image, our model can generate diverse grasps with multiple collision-free grasp orientations. Experimental evaluations in simulation and on a real robotic bin picking setup demonstrate the model's ability to generalize across various object categories achieving an object clearing rate of around $90 \%$ in simulation and real-world experiments. We also outperform state of the art approaches. Moreover, the proposed approach exhibits its usability in real robot experiments without any refinement steps, even when only trained on a synthetic dataset, due to the probabilistic grasp distribution modeling.<|reference_end|>
@article{liu2024efficient,
    title={Efficient End-to-End Detection of 6-DoF Grasps for Robotic Bin Picking},
    author={Yushi Liu (1), Alexander Qualmann (1), Zehao Yu (2), Miroslav Gabriel
  (1), Philipp Schillinger (1), Markus Spies (1), Ngo Anh Vien (1), Andreas
  Geiger (2) ((1) Bosch Center for Artificial Intelligence, Renningen, Germany,
  (2) University of Tuebingen, Tuebingen AI Center, Germany)},
    journal={arXiv preprint arXiv:2405.06336},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06336},
    primaryClass={cs.RO}
}