OrbitGrasp: $SE(3)$-Equivariant Grasp Learning
<|reference_start|>OrbitGrasp: $SE(3)$-Equivariant Grasp Learning: While grasp detection is an important part of any robotic manipulation pipeline, reliable and accurate grasp detection in $SE(3)$ remains a research challenge. Many robotics applications in unstructured environments such as the home or warehouse would benefit a lot from better grasp performance. This paper proposes a novel framework for detecting $SE(3)$ grasp poses based on point cloud input. Our main contribution is to propose an $SE(3)$-equivariant model that maps each point in the cloud to a continuous grasp quality function over the 2-sphere $S^2$ using a spherical harmonic basis. Compared with reasoning about a finite set of samples, this formulation improves the accuracy and efficiency of our model when a large number of samples would otherwise be needed. In order to accomplish this, we propose a novel variation on EquiFormerV2 that leverages a UNet-style backbone to enlarge the number of points the model can handle. Our resulting method, which we name $\textit{OrbitGrasp}$, significantly outperforms baselines in both simulation and physical experiments.<|reference_end|>
@article{hu2024orbitgrasp:,
    title={OrbitGrasp: $SE(3)$-Equivariant Grasp Learning},
    author={Boce Hu, Xupeng Zhu, Dian Wang, Zihao Dong, Haojie Huang, Chenghao
  Wang, Robin Walters, Robert Platt},
    journal={arXiv preprint arXiv:2407.03531},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03531},
    primaryClass={cs.RO}
}