Source-linked AI summary
DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness
Yiming Zhong, Qi Jiang, Jingyi Yu, Yuexin Ma
TL;DR
Dexterous grasping is difficult because high-dimensional hands must generate usable poses across diverse objects. DexGrasp Anything integrates physical constraints into diffusion-model training and sampling, and reports state-of-the-art performance together with a large grasping dataset. Its reported limitation is sub-optimal penetration-prone poses for extremely thin objects.
Problem
Generating robust, high-quality dexterous grasping poses is challenging because hands have many degrees of freedom and objects are highly diverse.
Method
DexGrasp Anything integrates three physical constraint objectives into a diffusion model during both training and sampling.
Results
The method achieves state-of-the-art performance on five dexterous grasping datasets and introduces a dataset with over 3.4 million poses across 15,698 objects.
Takeaways & Limitations
The method and dataset improve the reported quality, diversity, robustness, and generalization of dexterous grasp generation.
Takeaways & Limitations
The method produces sub-optimal poses with obvious penetration for extremely thin objects such as masks and plates.
Abstract
from arXiv · showhide
A dexterous hand capable of grasping any object is essential for the development of general-purpose embodied intelligent robots. However, due to the high degree of freedom in dexterous hands and the vast diversity of objects, generating high-quality, usable grasping poses in a robust manner is a significant challenge. In this paper, we introduce DexGrasp Anything, a method that effectively integrates physical constraints into both the training and sampling phases of a diffusion-based generative model, achieving state-of-the-art performance across nearly all open datasets. Additionally, we present a new dexterous grasping dataset containing over 3.4 million diverse grasping poses for more than 15k different objects, demonstrating its potential to advance universal dexterous grasping. The code of our method and our dataset will be publicly released soon.
1. Introduction
DexGrasp Anything addresses the difficulty of generating robust, diverse dexterous grasps by combining diffusion generation with physical constraints and expanding training data. The method integrates constraints during training and sampling, while its dataset scales to millions of poses across thousands of objects.
- Dexterous hands support flexible, precise, and versatile manipulation of diverse objects and human-designed tools.
- Analytical grasping methods struggle with dexterous hands’ large search space and high-dimensional optimization, producing low success rates.
- Diffusion models address grasp-pose diversity by iteratively transforming a simple distribution into a complex distribution.
- DexGrasp Anything integrates three physical constraint objectives into a diffusion model during both training and sampling.
- The method introduces surface-pulling, external-penetration-repulsion, and self-penetration-repulsion forces to improve grasp feasibility, hand-object interaction accuracy, and hand geometry.
- Over 3.4 million grasping poses across more than 15k objects form a large, diverse dataset intended to improve dexterous grasping research.
2. Related Work
Prior dexterous grasping work spans analytical, data-driven, generative, and diffusion-based methods, alongside simulator, optimization, and human-operated data collection. These approaches trade off search complexity, grasp diversity, dataset breadth, success rate, and collection cost.
- Analytical methods optimize grasp poses under physical constraints but face large search spaces and complex optimization, leading to low success rates.
- Regression-based methods can produce limited grasp diversity because direct prediction may fail to explore the full range of configurations.
- Generative methods model conditional hand-pose distributions and are intended to generate more diverse grasp poses.
- Existing grasping datasets are limited by narrow distributions, restricted object categories, scalability issues, and expensive 3D pose collection.
- Search-based datasets may remain narrow because they use low-dimensional eigengrasp spaces, while optimization-based datasets have relatively low success rates and few object categories.
- The DGA dataset combines data from multiple sources to provide diverse grasping poses for data-driven grasp generators.
3. Method
DexGrasp Anything models dexterous grasp poses with a diffusion process and integrates physical constraints during training and sampling. Its constraints address hand–object feasibility, penetration, and physically plausible hand geometry, while semantic object features further condition generation.
- Diffusion formulation: DexGrasp Anything models P(h|O) with a diffusion process that transforms isotropic Gaussian noise into a desired grasp-pose distribution.A grasp pose includes hand articulation, global rotation, and global translation.
- Physical constraints: Three physical objectives address surface feasibility, external hand–object penetration, and self-penetration between hand points.The objectives respectively pull nearby inner phalange surfaces toward the object, penalize undesired intersections, and enforce minimum distances between hand points.
- Physics-aware training: The standard diffusion MSE lacks explicit physical supervision, motivating a physics-aware training objective that combines denoising loss with weighted physical constraints.The constraints are applied through an estimated clean sample reconstructed from corrupted diffusion states.
- Physics-guided sampling: During sampling, posterior refinement repeatedly adjusts estimated clean grasps under physics constraints to steer generation toward feasible configurations.The physics-guided sampler extends classifier-guidance ideas to objective functions on clean samples and uses a guidance strength.
- Object representation: Object conditioning combines Point Transformer geometry features with semantic priors generated from a large language model.The Point Transformer encodes object point clouds, while a prompt based on the object label supplies semantic information.
4. Dataset
The DGA dataset combines diverse curated and generated grasping data into a large, broad-coverage resource for dexterous grasping research. Its object and pose distributions are broader than those of existing datasets.
- Data construction: DGA combines simulated, real-world, and retargeted human-hand data to increase grasping-data diversity and richness.The construction includes three simulated datasets, a human-operated real-world dataset, and GRAB retargeted to dexterous-hand parameters.
- Dataset characteristics: t-SNE visualization compares object features from DGA and existing datasets using randomly sampled objects and dataset-specific markers and colors.The visualization samples 5% of objects from each dataset for clarity.
- Dataset scale: Over 3.4 million grasping poses span 15,698 objects, including approximately 0.88 million curated and 2.52 million generated poses.The generated component covers 10,034 objects and 6,994 unique tags, while the curated component covers 5,664 objects.
- Dataset characteristics: DGA is intended to exceed existing datasets in scale, object diversity, and grasp-pose variation.Its stated characteristics include more than 3.4M strictly tested poses and 15,698 objects from diverse categories and sources.
- Dataset characteristics: DGA’s object features spread more widely across feature space, while its broader object range supports more diverse grasping poses.The paper reports that experiments show increased outcome diversity while maintaining or improving grasping success rates.
5. Experiments
Experiments evaluate grasp quality, diversity, physical-constraint ablations, cross-dataset generalization, and real-world deployment. The reported results show accurate, diverse, robust, and stable grasps across simulated and real settings.
- 5.1. Comparison: Grasp success, penetration, and diversity are evaluated using Suc. 6, Suc. 1, Pen., and Div. metrics in simulation.Success requires less than 2 cm object displacement under external force in one or all six axis-aligned directions.
- 5.1. Comparison: DexGrasp Anything achieves the highest or near-highest performance across most reported metrics and produces more accurate grasping poses.The qualitative results attribute the pose quality to physical constraints introduced during training and sampling.
- 5.3. Cross-dataset evaluation: Cross-dataset evaluation compares models trained on DexGraspNet with models trained on DGA across DexGraspNet and RealDex.The comparison evaluates dataset quality and cross-dataset generalization, with the best result within each group highlighted.
- 5.2. Ablation study: The ablation study incrementally adds three physical constraints and LLM-enhanced representation, and compares against physical-aware training without the guided sampler.The study is conducted on the DexGraspNet test set, with quantitative results in Table 3 and qualitative results in Figure 5.
- 5.4. Real-world Application: Real-world ShadowHand experiments produce reasonable and stable grasping motions for unseen real objects.The deployed system uses a pre-grasping motion sequence generated following the approach in [19].
6. Conclusions
DexGrasp Anything combines physics-aware diffusion generation with a large, diverse dexterous-grasping dataset. The paper reports improved grasp quality and diversity, with supplementary evaluations extending the method and dataset evidence.
- Contributions: DexGrasp Anything is a physics-aware diffusion generator for universal and robust dexterous grasp generation.It incorporates three tailored physical constraints through physics-aware training and a physics-guided sampler.
- Contributions: The paper presents the largest and most diverse dexterous-grasping dataset to date.The dataset is presented alongside the generator as a central contribution.
- Conclusion: Extensive experiments report improved quality and diversity of dexterous grasp generation from the method and dataset.Additional supplementary results include cross-dataset comparisons of diversity and all-direction grasp success rates.
B.1. Results for Cross-dataset Evaluation
Cross-dataset evaluation shows that training on the large-scale, diverse dataset improves generation diversity while maintaining comparable or higher grasping success rates than training on the original datasets.
- Training on the large-scale, diverse dataset significantly enhances generation diversity across the evaluated datasets.
- Training on the new dataset achieves comparable or higher grasping success rates than training on the respective original datasets.
- The evaluation covers five existing datasets and the newly introduced dataset.
B.2. Qualitative Results for Comparisons
The section compares DexGrasp Anything with existing state-of-the-art methods using qualitative visualizations of generated grasping poses.
- Additional qualitative results compare DexGrasp Anything with existing state-of-the-art methods.
- Figure 9 presents qualitative visualizations of comparisons on grasping poses.
B.3. More Visualizations for Ablation Studies
The ablation visualizations progressively add three physical constraints and LLM enhancement, while additional results show poses on challenging objects.
- More Visualizations for Ablation Studies: The ablation study progressively adds SRF, ERF, and SPF constraints during diffusion-generator training and sampling.Column (a) is the baseline, while columns (b), (c), and (d) add SRF, ERF, and SPF incrementally.
- More Visualizations for Ablation Studies: Column (e) shows results after incorporating LLM enhancement into the representation extraction process.
- More Visualizations of Generated Poses: Additional visualizations show generated grasping poses on challenging and irregular objects, including a robot model and a loong head.
- More Visualizations for Ablation Studies: Table 5 reports cross-dataset evaluation results, marking best values in bold and second-best values with underlining.
- More Visualizations for Ablation Studies: Figure 10 organizes each pair of rows as different views of the same grasp for the same object.
C. Implementation details
The dataset evaluation checks whether objects are held firmly without significant penetration using two hand-object penetration computation approaches; failed cases are visualized separately.
- Implementation details: Each dataset grasp pose is evaluated for firm object holding without significant penetration.
- Implementation details: One penetration approach computes each hand point’s Euclidean distance to its nearest object-surface neighbor.
- Implementation details: Figure 11 provides visualizations of failed cases.
D. Limitations and Future Works
The method has a remaining failure mode on extremely thin objects, where it can produce visibly penetrating, sub-optimal grasp poses. The authors suggest improved affordance modeling or tactile feedback as future directions.
- Extremely thin objects, such as masks and plates, can produce sub-optimal poses with obvious penetration.
- The authors identify enhanced affordance modeling as a promising direction for addressing these thin-object grasping failures.
- Integrating tactile feedback into the robotic grasping system is another proposed direction for addressing the observed challenges.
- The cross-dataset evaluation visualization places single-dataset-trained models in the top row and models trained on the authors’ dataset in the bottom row.
- Figures 13 and 14 provide visualizations of the method’s results without stating an additional comparison in the supplied passages.