GraspFoM: Towards Reconstruction-Driven Robotic Grasping with 3D Foundation Priors
GraspFoM leverages SAM3D-based 3D priors for reconstruction-driven robotic grasping, achieving state-of-the-art AP and reconstruction metrics.
Key Findings
Methodology
This approach constructs a shared 3D object latent space from SAM3D, integrating a truncated anchor-conditioned diffusion model for continuous, multi-modal grasp pose prediction. It introduces a reconstruction-aware scorer and residual latent updater to ground grasp predictions in geometric cues and refine object representations. The end-to-end training optimizes both high-fidelity reconstruction and grasp accuracy, outperforming existing methods.
Key Results
- On GraspNet-1Billion, the model achieves XX% AP, XX% AP0.8, and XX% AP0.4, surpassing ZeroGrasp and MG-Grasp. Chamfer Distance drops to XX, F1-score rises to XX, indicating superior geometric fidelity. Multi-modal grasp pose accuracy improves by XX%, demonstrating strong generalization.
Significance
This work advances robotic manipulation by integrating 3D priors directly into grasp prediction, addressing partial observation challenges. It enhances robustness and generalization, enabling robots to operate reliably in cluttered, occluded environments, with broad implications for automation and industry.
Technical Contribution
The paper introduces a unified framework combining SAM3D-based shared object latent, anchor-conditioned diffusion for continuous grasp prediction, and a reconstruction-aware scoring mechanism. The residual latent updater further couples geometry and manipulation cues, with minimal parameter overhead, enabling scalable, high-precision grasping in complex scenes.
Novelty
First to embed large-scale 3D foundation priors directly into a multi-modal, continuous grasp prediction pipeline for robotics. The anchor-initialized truncated diffusion model, combined with geometry-aware scoring, offers a novel solution to the limitations of discrete candidate-based methods, setting a new state-of-the-art.
Limitations
- Performance under extreme occlusion and highly sparse observations remains limited. The inference speed is affected by the diffusion process, hindering real-time deployment. Handling dynamic scenes and multi-object interactions needs further research.
Future Work
Future directions include integrating multi-sensor data (force, tactile), optimizing diffusion sampling for speed, and extending to dynamic, multi-object scenarios. Enhancing robustness in real-world cluttered environments will be a key focus.
AI Executive Summary
Robotic grasping has long been a cornerstone of automation, yet remains challenging under partial and occluded observations. Traditional methods rely heavily on geometric reconstruction or discrete candidate sampling, which struggle in cluttered, real-world environments. These approaches often lack the ability to predict smooth, multi-modal grasp poses directly from incomplete data, limiting their robustness and adaptability.
This paper introduces GraspFoM, a novel framework that leverages the powerful SAM3D foundation priors to build a shared 3D object latent space. This latent serves as the backbone for both high-fidelity object reconstruction and continuous, multi-modal grasp pose prediction. The core innovation is a truncated anchor-conditioned diffusion model that directly generates diverse grasp hypotheses without relying on discrete candidates. To ground these predictions in geometric reality, the authors incorporate a reconstruction-aware scoring mechanism that evaluates graspability, quality, and affordance based on the reconstructed geometry. Additionally, a residual latent updater refines the object representation by injecting manipulation-relevant cues, creating a bidirectional feedback loop between reconstruction and grasping.
Experimental results on the GraspNet-1Billion benchmark demonstrate that GraspFoM achieves state-of-the-art performance, with significant improvements in grasp detection accuracy and geometric fidelity metrics. The model not only outperforms existing methods like ZeroGrasp and MG-Grasp but also maintains high efficiency with minimal parameter overhead. These advances suggest that the integration of large-scale 3D priors into grasping pipelines can dramatically enhance robustness, generalization, and manipulation success in complex environments.
Despite these achievements, challenges remain in handling extreme occlusions, real-time inference, and dynamic scenes. Future work will focus on multi-sensor fusion, accelerated sampling techniques, and broader application scenarios. Overall, GraspFoM marks a significant step toward intelligent, adaptable robotic manipulation, bridging the gap between 3D perception and precise, reliable grasping.
Deep Analysis
Background
Robotic manipulation has evolved from simple rule-based systems to deep learning-driven approaches. Early methods like Fang et al. (2020) focused on direct pose prediction from RGB-D data, relying on discrete grasp candidates. Recent advances incorporate 3D reconstruction techniques such as implicit neural representations (Wang et al., 2021; Yariv et al., 2021) and large-scale models like SAM3D (Yang et al., 2023), which learn strong object priors from massive datasets. These models enable more accurate scene understanding and shape recovery, but their application to manipulation remains limited by the gap between digital asset fidelity and manipulation-relevant features like grasp affordances.
Core Problem
Existing methods either focus on geometric fidelity without considering manipulation cues or rely on discrete candidate sampling, which limits robustness in partial, occluded scenes. The challenge is to develop a unified approach that leverages high-quality 3D priors for continuous, multi-modal grasp prediction while grounding predictions in geometric reality. Achieving this requires integrating reconstruction, grasp reasoning, and manipulation affordance understanding into a single, scalable framework.
Innovation
This work introduces a shared object latent derived from SAM3D, enabling joint reconstruction and grasp prediction. It employs an anchor-initialized truncated diffusion model to generate diverse, continuous grasp poses directly from the latent, avoiding discrete candidate limitations. The addition of a reconstruction-aware scorer evaluates grasp hypotheses based on geometric cues, while a residual latent updater refines object representations with manipulation-relevant signals. These innovations collectively improve generalization, efficiency, and prediction quality.
Methodology
- �� Use SAM3D’s stage-1 output to extract a compact shared latent space representing the object. • Convert the latent into a shape-aware 3D grid for local and global feature extraction. • Query surface points and normals, fuse local geometry with shape context via cross-attention. • Initialize an anchor set through K-means clustering on training grasp poses. • Model grasp prediction as a truncated diffusion process conditioned on anchors, predicting continuous, multi-modal poses. • Incorporate a grasping scorer predicting graspability, quality, and affordance grounded in reconstructed geometry. • Use a latent updater to refine the object representation based on grasp supervision, enhancing manipulation cues.
Experiments
The model was trained and evaluated on GraspNet-1Billion, measuring AP, Chamfer Distance, and F1-score. Ablation studies confirmed the contributions of each component. Comparisons with baselines like ZeroGrasp and MG-Grasp demonstrated superior grasp accuracy and reconstruction quality. Hyperparameters were tuned for robustness across seen, similar, and novel object splits, with additional tests on real-world clutter scenarios. The evaluation confirmed the model’s ability to generate diverse, high-quality grasp hypotheses grounded in accurate 3D reconstructions.
Results
GraspFoM achieved XX% AP, surpassing baselines by XX%. Chamfer Distance was reduced to XX, indicating finer geometric reconstruction. The multi-modal grasp pose accuracy increased by XX%, demonstrating effective diversity and robustness. Ablation results showed that removing the reconstruction scorer or latent updater degraded performance by XX%, highlighting their importance. The model maintained real-time inference capability with minimal parameter increase, confirming efficiency.
Applications
This framework can be deployed in industrial robots for bin picking, warehouse automation, and service robots in domestic environments. It requires only RGB images and masks, compatible with existing sensor setups. The ability to generate diverse, manipulation-relevant grasp poses makes it suitable for complex, cluttered scenes, improving success rates and operational reliability.
Limitations & Outlook
Current performance drops under extreme occlusion or highly sparse observations. The diffusion process introduces latency, limiting real-time deployment. Handling dynamic scenes with moving objects remains challenging. Future work should focus on multi-sensor fusion, faster sampling, and adaptation to real-world variability.
Plain Language Accessible to non-experts
想象你在厨房准备一顿饭，你面对各种食材：蔬菜、肉类、调料。每次你都要找到合适的食材、拿起刀切割，然后放到锅里。这篇论文就像发明了一个超级聪明的厨房助手，它可以提前记住每种食材的3D形状和位置，甚至在被遮挡或藏得很深时也能找到它们。这个助手不仅知道每个食材的完整形状，还能告诉你用多大的力气、从哪个角度切，确保每一步都顺利完成。它用一种特殊的“记忆库”存储所有食材的3D信息，然后用一种聪明的“猜测”方法，快速给出最佳操作方案。这样一来，无论食材藏得多深、被遮挡得多厉害，助手都能帮你顺利完成任务。这个技术让机器人像个聪明的厨师一样，能在复杂环境中找到正确的食材和操作方式，大大提高效率和成功率。
ELI14 Explained like you're 14
想象你在玩一个拼图游戏，但拼图块被遮住了一部分，你需要猜出剩下的部分。这个论文里的机器人就像一个超级拼图高手，它有一个特别的“脑袋”，里面装满了各种拼图的3D模型。它能根据看到的部分，快速猜出完整的形状，然后用这个信息找到最合适的抓取姿势。它不像以前那样只用猜测几个固定的姿势，而是能连续地、用不同的方式猜出很多可能的抓取方法，就像你在游戏中试不同的拼图组合一样。它还会根据拼图的完整度，给每个猜测打分，选出最靠谱的那一个。这样一来，即使拼图被遮住一部分，机器人也能稳稳地抓住目标。这个技术让机器人变得更聪明、更灵活，可以在复杂、遮挡很多的环境中完成任务，就像你在玩拼图时变得更厉害一样。
Abstract
Robotic grasping is a fundamental capability in robotic manipulation. Yet grasping remains challenging under partial observations. Reliable grasping depends on both local contact cues and object-level 3D structure. Existing geometry-aware grasping methods recognize the value of reconstruction, but they typically treat geometry as an intermediate prediction rather than a reusable object prior for grasping. In this paper, we present GraspFoM, a unified framework that leverages 3D foundation priors (SAM3D) to build a shared 3D object latent for both reconstruction and grasp pose prediction. Built on this shared object latent, we introduce an anchor-initialized truncated pose-reasoning diffuser that predicts continuous and multimodal grasp poses without directly relying on discrete grasp candidates. We further investigate the interaction between reconstruction and grasping through a reconstruction-aware scorer and a residual latent updater. Reconstruction provides grounded geometric cues, while grasp supervision refines the shared object latent toward grasp-relevant affordances. GraspFoM jointly predicts grasp poses and reconstructs high-fidelity 3D assets in mesh and 3DGS forms. Comprehensive experiments demonstrate that GraspFoM achieves state-of-the-art results on both reconstruction and grasping. Notably, these improvements require only a small number of additional trainable parameters. Component-wise ablation studies also demonstrate the contribution of each component.