Constrained 6-DoF Grasp Generation on Complex Shapes for Improved Dual-Arm Manipulation
CGDF: A diffusion-based method for dense, constrained 6-DoF grasping on complex shapes, achieving over 60% success in challenging scenarios.
Key Findings
Methodology
This paper introduces CGDF, which employs an energy-based SE(3) diffusion model combined with local geometric features encoded via convolutional planes. The model uses a point cloud encoder like VN-PointNet to extract local features, projecting them onto three orthogonal planes. An energy network evaluates grasp stability, trained in an unconditional manner with noise prediction. For constrained grasping, a part-guided diffusion strategy guides samples toward target regions by comparing energies on full object and target point clouds, enabling dense grasp generation without large annotated datasets. The approach supports multi-region and multi-arm scenarios, trained on the DA2 dataset, demonstrating high generalization and efficiency.
Key Results
- On the DA2 dataset, CGDF achieves grasp success rates of 60.3% (unconstrained) and 60.88% (constrained), outperforming VCGS and SE3Diff. It excels in complex shapes like chairs and lamps, with target region coverage reaching 91.86%. The model demonstrates strong generalization to large, intricate objects, with dense grasp coverage and high stability in dual-arm setups.
- Ablation studies show convolutional plane features significantly reduce Chamfer distance (from 60.21 to 14.04), indicating better local geometry encoding. The part-guided diffusion effectively directs grasps to target regions, improving the ratio of grasps on desired areas and stability metrics.
- In dual-arm stability tests, CGDF attains force closure of 44.8% and grasp success rate of 60.88%, surpassing existing methods. Its robustness in simulation highlights its potential for real-world applications, especially in complex industrial tasks requiring dense, stable multi-region grasps.
Significance
This work advances robotic grasping by enabling dense, stable, and targeted grasp generation on complex objects, especially for dual-arm systems. By leveraging an energy-based diffusion framework and local geometric features, it overcomes limitations of prior global-shape-dependent methods, reducing reliance on large annotated datasets. The approach enhances the robot's ability to operate in cluttered, complex environments, broadening industrial automation, service robotics, and multi-arm manipulation. Its high generalization and sample efficiency mark a significant step toward autonomous, adaptable robotic systems capable of handling real-world complexity with minimal supervision.
Technical Contribution
The core innovation lies in integrating an energy-based SE(3) diffusion model with local geometric features encoded via convolutional planes, enabling high-fidelity shape representation and dense grasp sampling. The model's key components include: β’ a point cloud encoder (VN-PointNet) for local feature extraction; β’ a multi-plane convolutional encoder for geometric detail; β’ an energy network trained in an unconditional manner; β’ a part-guided diffusion strategy that compares energies on full and target point clouds to guide grasp sampling. This framework allows for efficient, high-quality grasp generation on complex shapes without large constraint datasets, supporting multi-region and multi-arm tasks.
Novelty
This is the first application of an energy-based SE(3) diffusion model combined with local geometric encoding for complex shape constrained grasping. Unlike prior methods relying on large annotated datasets, CGDF employs a part-guided strategy that uses energy evaluations to steer grasp samples toward target regions. This approach significantly improves sample efficiency, generalization to large and intricate objects, and multi-region grasping, representing a novel paradigm shift in robot manipulation research.
Limitations
- The model struggles with extremely thin or elongated structures where local features are insufficient to capture geometry, leading to less accurate grasps.
- High computational cost during training and inference limits real-time deployment on resource-constrained robots.
- Current validation is primarily in simulation; real-world robustness, especially under sensor noise and dynamic environments, remains to be tested.
Future Work
Future directions include integrating multi-modal sensory data (RGB-D, semantic cues) to enhance local geometry understanding, reducing computational overhead for real-time applications, and deploying on physical robotic systems. Additionally, extending the framework to multi-robot coordination and dynamic environments will broaden its practical impact, aiming for fully autonomous multi-arm manipulation in cluttered, unstructured settings.
AI Executive Summary
Robotic grasping has long been a cornerstone of automation, yet existing methods often falter when faced with complex, irregular objects, especially in multi-arm scenarios. Traditional algorithms typically generate uniform grasps over entire objects, which is inefficient and unreliable for fragile or large items requiring targeted contact points. To address this, the authors introduce CGDF, a novel approach leveraging an energy-based SE(3) diffusion model combined with local geometric features encoded via convolutional planes. This framework enables dense, stable grasp generation on specific target regions of complex shapes without relying on large annotated constraint datasets. The core innovation lies in the part-guided diffusion strategy, which compares energies computed on full objects and target regions, effectively guiding grasps to desired areas. Extensive experiments on the DA2 dataset demonstrate that CGDF achieves over 60% success rates in both unconstrained and constrained settings, outperforming state-of-the-art methods like VCGS and SE3Diff. Notably, the model excels in handling large, intricate objects such as chairs and lamps, with target region coverage reaching nearly 92%. In dual-arm simulations, it attains high stability and low collision rates, showcasing its potential for industrial and service robotics. The approach's ability to generalize across diverse geometries and efficiently generate dense grasp sets marks a significant advance in robotic manipulation. Despite these achievements, challenges remain in real-world deployment, particularly regarding computational costs and robustness under sensor noise. Future work aims to integrate multi-modal sensing, optimize inference speed, and extend to multi-robot coordination, paving the way for autonomous robots capable of complex, targeted manipulation in unstructured environments.
Deep Dive
Abstract
Efficiently generating grasp poses tailored to specific regions of an object is vital for various robotic manipulation tasks, especially in a dual-arm setup. This scenario presents a significant challenge due to the complex geometries involved, requiring a deep understanding of the local geometry to generate grasps efficiently on the specified constrained regions. Existing methods only explore settings involving table-top/small objects and require augmented datasets to train, limiting their performance on complex objects. We propose CGDF: Constrained Grasp Diffusion Fields, a diffusion-based grasp generative model that generalizes to objects with arbitrary geometries, as well as generates dense grasps on the target regions. CGDF uses a part-guided diffusion approach that enables it to get high sample efficiency in constrained grasping without explicitly training on massive constraint-augmented datasets. We provide qualitative and quantitative comparisons using analytical metrics and in simulation, in both unconstrained and constrained settings to show that our method can generalize to generate stable grasps on complex objects, especially useful for dual-arm manipulation settings, while existing methods struggle to do so.