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GrainGrasp: Dexterous Grasp Generation with Fine-grained Contact Guidance
Fuqiang Zhao, Dzmitry Tsetserukou, Qian Liu
TL;DR
Coarse or ambiguous contact maps and the missing link between predicted contacts and executed grasps make dexterous grasp generation challenging. GrainGrasp predicts distinct contact maps for each fingertip and uses DCoG to guide grasp optimization. The authors report higher success rates and perceptual scores than other methods, while noting that performance depends on the CVAE’s contact-map generation.
Problem
Typical predicted contact maps can be coarse or ambiguous, and fine-grained maps alone do not guarantee human-desired grasp positions without explicit contact-to-grasp modeling.
Source: §I · p. 1Method
GrainGrasp predicts a separate contact map for each fingertip and applies DCoG to model contact-to-grasp relationships through directional consistency.
Source: §I · pp. 1–2Results
The authors report that GrainGrasp’s success rate and perceptual scores surpass those of other methods.
Source: A. Quantitative Evaluations · p. 6Takeaways & Limitations
The method can determine each finger’s contribution to the final grasp, a capability the authors say prior methods did not present.
Source: §VI · p. 7The proposed scheme’s performance depends on the CVAE’s ability to generate contact maps.
Source: §VI · p. 7
Abstract
from arXiv · showhide
One goal of dexterous robotic grasping is to allow robots to handle objects with the same level of flexibility and adaptability as humans. However, it remains a challenging task to generate an optimal grasping strategy for dexterous hands, especially when it comes to delicate manipulation and accurate adjustment the desired grasping poses for objects of varying shapes and sizes. In this paper, we propose a novel dexterous grasp generation scheme called GrainGrasp that provides fine-grained contact guidance for each fingertip. In particular, we employ a generative model to predict separate contact maps for each fingertip on the object point cloud, effectively capturing the specifics of finger-object interactions. In addition, we develop a new dexterous grasping optimization algorithm that solely relies on the point cloud as input, eliminating the necessity for complete mesh information of the object. By leveraging the contact maps of different fingertips, the proposed optimization algorithm can generate precise and determinable strategies for human-like object grasping. Experimental results confirm the efficiency of the proposed scheme.
I. INTRODUCTION
Existing contact-map predictions can be too coarse or ambiguous for stable grasps on complex objects, while fine-grained contacts alone do not guarantee human-desired grasp poses. GrainGrasp predicts a distinct map for each fingertip and uses DCoG to guide grasp generation through finger-level directional consistency.
I. INTRODUCTION
- Coarse or ambiguous contact maps and the lack of explicit contact-to-grasp modeling leave stable, human-desired grasps challenging, particularly for complex objects.The introduction identifies detailed adjustment of individual fingers as a need for high-quality grasp generation.Source: §I · p. 1
- GrainGrasp predicts a distinct contact map for each fingertip, capturing finger-object contact details rather than a holistic map or a few contact points.The approach focuses on each finger’s contribution and coordination instead of regulating the whole hand.Source: §I · pp. 1–2
- The pipeline generates individual contact maps, optimizes an initialized hand pose using the object point cloud and maps, then adjusts finger contributions for determinable grasps.The figure caption describes these as three stages.Source: Figure 2 · p. 2
- DCoG models the relationship between contact maps and grasp determination through directional consistency between each finger and the object surface.The method is intended to expose each finger’s role and its coordination with other fingers.Source: §I · p. 2
II. RELATED WORK
Prior dexterous grasp synthesis spans analytical optimization and data-driven prediction. Data-driven methods learn grasp parameters or contact information from datasets, while analytical methods commonly optimize poses for force closure under physical constraints.
A. Dexterous Grasp Synthesis
- The synthesis task is challenging because dexterous hands have many degrees of freedom and grasp interactions are complex.Source: A. Dexterous Grasp Synthesis · p. 2
- Analytical grasp-synthesis methods typically optimize grasp pose for force closure while considering physical constraints.The cited examples include a differentiable force-closure estimator and refinements to initial hand poses.Source: A. Dexterous Grasp Synthesis · p. 2
- Data-driven methods use neural networks and grasp datasets to predict grasp parameters or contact information, including points, maps, regions, and trajectories.Examples include grasp-parameter generators, contact-point selectors, contact-map estimators, and implicit grasping fields.Source: A. Dexterous Grasp Synthesis · p. 2
B. Contact-based Approaches
Contact-based approaches use object contact information to guide dexterous grasping. GrainGrasp predicts separate fingertip maps and uses point-cloud-based annotation and a CVAE to support finger-specific guidance.
B. Contact-based Approaches
- Separate contact maps for each fingertip allow independent optimization of each finger’s contact location and force, providing fine-grained grasp guidance.Source: B. Contact-based Approaches · p. 3
III. CONTACT MAP GENERATION
- A CVAE trained on automatically annotated ObMan data generates separate fingertip contact maps from an object point cloud.Source: §III · p. 3
A. Dataset Annotation
- The annotation procedure labels object points as contacted by the thumb, index, middle, ring, or pinky fingertip, or as uncontacted.Fingertip regions are delineated, nearby object points are assigned to the closest fingertip region, and the method relies on point-cloud data.Source: A. Dataset Annotation · p. 3
- Using Euclidean distance for point-cloud annotation can produce false contact maps on thin objects, while too small a K can make maps difficult to predict.The authors report satisfactory labeling with K=50 for a 3000-point cloud.Source: A. Dataset Annotation · p. 3
B. CVAE Synopsis
The CVAE treats contact-map generation as point-cloud segmentation and predicts separate fingertip maps. Its training combines contact-map reconstruction losses with latent-space regularization.
B. CVAE Synopsis
- The CVAE generates separate fingertip contact maps by treating map generation as multiclass pointwise segmentation.Source: B. CVAE Synopsis · p. 3
- The encoder combines object-point features with embedded contact-map category features to produce a latent distribution, and the decoder combines sampled latent codes with pointwise features to generate the map.The described encoder uses PointNet, an MLP, and max pooling; the decoder uses an MLP.Source: B. CVAE Synopsis · p. 3
- Training uses reconstruction and regularization losses, with reconstruction combining multiclass cross-entropy and dice losses and regularization using KL loss.The stated weights are 0.5, 0.9, and 0.01 for cross-entropy, dice, and KL losses, respectively.Source: B. CVAE Synopsis · pp. 3–4
IV. PROPOSED GRASPING OPTIMIZATION
DCoG uses object point clouds and contact maps to optimize hand direction and grasp distance through multiple energy terms. Its terms guide fingertip contact and finger direction, with adaptive weighting and additional grasp-success and penetration terms.
IV. PROPOSED GRASPING OPTIMIZATION
- DCoG optimizes hand direction and grasp distance from an object point cloud and its contact map.It uses multiple energy terms to produce deterministic grasping results.Source: §IV · p. 4, A. Optimization Method · p. 4
A. Optimization Method
- Edis minimizes squared distances between fingertip points and contact-map points in the same category.The fingertip categories are manually delineated, while object contact-map categories are generated by the CVAE.Source: A. Optimization Method · p. 4
- Edct and Edcf measure directional consistency at the fingertip and finger, respectively, using surface normals and directions toward nearest object points.The two terms are combined into directional consistency energy Edc.Source: A. Optimization Method · p. 4
- The adaptive schedule changes the relative weights of distance and directional consistency energies over iterations to promote a more natural grasping process.The initial phase assigns lower weight to Edis and higher weight to Edc; as iterations increase, Edis weight decreases and Edc weight increases.Source: A. Optimization Method · pp. 4–5
- Figure 4 shows grasps from the complete method, the optimization-only method, and Ground Truth.The complete method includes predicted contact maps, whereas the optimization-only method uses Ground Truth-annotated contact maps.Source: Figure 4 · p. 5
- A grasp-success network energy and penetration energy supplement the optimization; penetration is estimated without signed distances because the method relies on point clouds.The PointNet-based binary classifier takes hand and object point clouds and predicts grasp success; Epen follows Jiang et al..Source: A. Optimization Method · pp. 4–5
B. Initialization Strategy
Initialization varies palm orientation and selects the final grasp by minimum penetration energy.
B. Initialization Strategy
- Random palm rotations add initialization diversity, and the result with minimum Epen is selected because incorrect grasps often penetrate the object substantially.The hand is initialized away from the object with its middle fingertip aligned to the contact map.Source: B. Initialization Strategy · p. 5
C. Analysis of Determinable Generation
Individual finger contributions can be customized by adjusting category weights in Edis.
C. Analysis of Determinable Generation
- Adjusting category weights in Edis lets the algorithm prioritize selected fingers for different grasping tasks.The method uses individualized contact and interpretable energy terms to customize finger contributions.Source: C. Analysis of Determinable Generation · p. 5
V. EXPERIMENT
The experiment section reports quantitative and qualitative comparisons and ablations of the method's modules.
V. EXPERIMENT
- The evaluation compares the proposed approach with other grasping methods and uses ablations to examine module contributions and their combination.Both quantitative and qualitative evaluations are conducted.Source: §V · p. 5
A. Quantitative Evaluations
The complete method achieves higher success rates and perceptual scores than other methods. The evaluation balances penetration and simulated object displacement, while the perceptual study uses full-view 3D grasp displays.
A. Quantitative Evaluations
- A grasp is successful when penetration volume is below 5 cm3 and object displacement is below 2 cm.The authors describe this definition as stringent and focused on grasp granularity.Source: A. Quantitative Evaluations · pp. 5–6
- The perceptual study asks 15 participants to score 20 randomly generated grasps using complete 3D views.ContactOpt is excluded for low success rate, and GA without TTA for obviously worse performance.Source: A. Quantitative Evaluations · p. 6
- The complete method surpasses other methods in success rate and perceptual scores.ContactOpt receives parameters generated by GrainGrasp; optimization-only evaluation uses annotated contact maps.Source: A. Quantitative Evaluations · p. 6
B. Qualitative Evaluations
Qualitative examples compare complete-method and optimization-only grasps with ground truth. The results illustrate stable grasps and controllable outcomes when finger contributions are adjusted.
B. Qualitative Evaluations
- Optimization-only grasps using ground-truth annotated contact maps closely resemble the ground truth, while complete-method grasps differ.The complete method generates its contact maps autonomously with a CVAE.Source: B. Qualitative Evaluations · p. 6
- Qualitative examples demonstrate that the proposed method produces stable grasps.The passage presents examples from the complete method, optimization-only method, and ground truth.Source: B. Qualitative Evaluations · p. 6
- Setting different fingers’ contributions to zero yields determinable grasp poses.The authors describe this controllability as potentially enhancing adaptability in grasping.Source: B. Qualitative Evaluations · p. 6
C. Ablation Study
The ablation study distinguishes the roles of four energy terms. The methodological analysis reports point-cloud input and finger control, while the conclusion notes dependence on CVAE contact-map generation.
C. Ablation Study
- Removing Edc sharply reduces successful grasps; Edct and Edcf primarily increase success rate, while Enet emphasizes close hand–object interaction.The ablation evaluates the proposed energy terms individually.Source: C. Ablation Study · p. 6
D. Methodological Analysis
- The method uses sampled object point clouds and permits adjustment of each finger’s contribution during grasping.The authors also report preserved small simulated object displacements while minimizing penetration volume.Source: D. Methodological Analysis · p. 6
- If the CVAE has not seen objects with similar shapes during training, inaccurate contact maps may lead to grasp failures.The authors identify comprehensive large-scale training as necessary to address this limitation.Source: D. Methodological Analysis · p. 6
VI. CONCLUSION
- GrainGrasp predicts individual finger contact maps with a CVAE and uses DCoG to guide grasp generation.The conclusion says performance depends on the CVAE’s contact-map generation capability.Source: §VI · pp. 6–7