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OrbitGrasp: $SE(3)$-Equivariant Grasp Learning (2407.03531v3)

Published 3 Jul 2024 in cs.RO

Abstract: 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 $\textit{OrbitGrasp}$, significantly outperforms baselines in both simulation and physical experiments.

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Citations (4)
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Summary

  • The paper introduces a novel SE(3)-equivariant grasp learning approach that uses a spherical harmonics basis with a modified EquiFormerV2 backbone to compute continuous grasp quality over S².
  • It leverages orbit evaluation relative to point normals to determine robust grasp poses, achieving superior performance over existing methods in simulation and real-world experiments.
  • It demonstrates the practical efficacy of integrating geometric equivariance for efficient robotic manipulation in complex, cluttered environments.

Overview of "OrbitGrasp: SE(3)-Equivariant Grasp Learning"

The paper "OrbitGrasp: SE(3)-Equivariant Grasp Learning" addresses the challenge of accurate grasp detection in unstructured environments using point cloud data. The authors propose a novel approach that leverages SE(3)SE(3)-equivariant models to improve grasp learning by mapping each point in a point cloud to a continuous grasp quality function over the 2-sphere S2S^2. This method aims to enhance the reliability and efficiency of grasp detection in scenes where the orientation of objects is a significant factor.

Contributions

The primary contributions of this paper are multifaceted:

  1. Spherical Harmonics Approach: The authors employ a spherical harmonic basis to represent the grasp quality function over S2S^2. This continuous representation allows the model to infer grasp quality across a continuous range of orientations, contrasting with traditional methods that rely on a finite set of samples.
  2. Enhanced Equivariance through EquiFormerV2: A modified version of EquiFormerV2 is utilized, incorporating a UNet-style backbone to handle a larger number of points. This structure facilitates better generalization and scalability, essential for processing complex point cloud data.
  3. OrbitGrasp Methodology: By evaluating the orbit of approach directions relative to each point's surface normal, the method efficiently determines high-quality grasp poses. The approach is devised to be computationally efficient and to integrate the symmetric nature of the grasping problem, leveraging SE(3)SE(3) equivariance.
  4. Empirical Validation: The method significantly outperforms existing baselines in both simulation and real-world experiments across various settings. Through benchmark tasks involving cluttered and structured object placements, the efficacy of OrbitGrasp in both single and multi-view camera configurations is demonstrated.

Results and Implications

The performance of OrbitGrasp is quantified through extensive experimental results. It achieves higher grasp success rates and declutter rates compared to existing state-of-the-art methods such as GIGA, VGN, and recent point cloud-based methods like EdgeGrasp and VNEdgeGrasp. This success is highlighted in both packed and piled object tasks, underlining the model's robustness in handling complex manipulation scenarios.

Moreover, the paper explores an ablation paper to discern the impact of model components, such as the spherical harmonic degree and the role of equivariance in grasp learning. These studies illustrate that higher-degree spherical harmonics can refine grasp prediction accuracy, and invariant modeling contributes substantially to handling the SO(3)SO(3) space effectively.

Future Directions and Impact

The authors propose several avenues for future exploration, including addressing inference speed optimization and incorporating constraints for specific grasp objectives. The paper suggests that leveraging gauge equivariance could enhance computation efficiency. Furthermore, potential developments may involve conditioning the model with language or visual cues to focus grasping on particular objects or parts, aligning with the ongoing trend toward multimodal AI systems.

In the broader context of robotics and AI, this paper contributes to advancing autonomous robotic manipulation, especially in dynamic or unstructured environments. The integration of SE(3)SE(3)-equivariant models represents a step toward more adaptable and intelligent systems capable of performing complex tasks with precision and reliability. The work lays the groundwork for future research that could extend these principles to other domains requiring robust spatial reasoning.

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