# OrbitGrasp: SE(3)-Equivariant Grasp Learning

arXiv: https://arxiv.org/abs/2407.03531 · HTML: https://mlsanity.com/paper/2407.03531/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2024.

## Built on by (most cited first, 16 of 16)

- [ET-SEED: Efficient Trajectory-Level SE(3) Equivariant Diffusion Policy](https://mlsanity.com/paper/2411.03990/) (2024)
- [Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulation](https://mlsanity.com/paper/2504.06961/) (2025)
- [SE(3)-Equivariant Robot Learning and Control: A Tutorial Survey](https://mlsanity.com/paper/2503.09829/) (2025)
- [SE(3)-Equivariant Diffusion Policy in Spherical Fourier Space](https://mlsanity.com/paper/2507.01723/) (2025)
- [MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies](https://mlsanity.com/paper/2409.15517/) (2024)
- [A Practical Guide for Incorporating Symmetry in Diffusion Policy](https://mlsanity.com/paper/2505.13431/) (2025)
- [3D Equivariant Visuomotor Policy Learning via Spherical Projection](https://mlsanity.com/paper/2505.16969/) (2025)
- [Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance](https://mlsanity.com/paper/2409.06171/) (2024)
- [Coarse-to-Fine 3D Keyframe Transporter](https://mlsanity.com/paper/2502.01773/) (2025)
- [Coordinated Humanoid Robot Locomotion with Symmetry Equivariant Reinforcement Learning Policy](https://mlsanity.com/paper/2508.01247/) (2025)
- [Generalizable Hierarchical Skill Learning via Object-Centric Representation](https://mlsanity.com/paper/2510.21121/) (2025)
- [MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping](https://mlsanity.com/paper/2507.02672/) (2025)
- [Equivariant Volumetric Grasping](https://mlsanity.com/paper/2507.18847/) (2025)
- [Learning Smooth State-Dependent Traversability from Dense Point Clouds](https://mlsanity.com/paper/2506.04362/) (2025)
- [Action Map Policy: Learning 3D Closed-loop Manipulation via Pixel Classification](https://mlsanity.com/paper/2607.10706/) (2026)
- [Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation](https://mlsanity.com/paper/2607.11167/) (2026)

## Abstract (arXiv)

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 S2 using spherical harmonic basis functions. 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 encoder-decoder architecture to enlarge the number of points the model can handle. Our resulting method, which we name textitOrbitGrasp, significantly outperforms baselines in both simulation and physical experiments.