cs.ROAug 20, 2026
SaveCoToGrasp: Contact-Topology-Conditioned Dexterous Grasp Synthesis via Canonical Workspace Learning
Organizations: Université Paris-Saclay, CEA, List, F-91120 Palaiseau, France · Ecole Centrale Lyon, CNRS, LIRIS, UMR5205, Institut Universitaire de France (IUF), F-69130 Ecully, France
Abstract
Current dexterous grasp planners primarily optimize for physical stability, focusing on whether an object can be grasped rather than how it should be grasped to support downstream functional tasks. However, conditioning grasp synthesis on specific human grasp taxonomies typically requires prohibitively expensive, object-annotated datasets. To address these limitations, we propose CoToGrasp, a novel generative framework that synthesizes diverse, stable grasps strictly conditioned on specific contact topologies. To bypass the data collection bottleneck, CoToGrasp is trained entirely in an object-agnostic manner. We introduce a feature-based canonical workspace that projects local object features into a unified gripper-centric domain, effectively decoupling the semantic functional intent from the arbitrary object geometry. By learning the intrinsic contact manifold of the gripper within this workspace, our model achieves zero-shot generalization to unseen objects at inference. Extensive evaluations on the large-scale DexGraspNet dataset demonstrate that CoToGrasp achieves state-of-the-art performance, outperforming existing taxonomy-guided planners. Finally, we demonstrate the physical viability and kinematic feasibility of our synthesized contact topologies on a physical robot platform. Code is available on our project website at https://cea-list.github.io/cotograspweb/ .
Figures & tables
Figure 1 : Contact-Topology-Conditioned Grasp Synthesis. Given a desired semantic contact-topology condition (top left) – categorized into Precision , Object-Specific (highly constrained topologies tailored for specific tool use) or Power functional groups – and a novel, unseen object (bottom left), our framework synthesizes functionally diverse and physically stable grasps (right). Rather than learning contact topologies directly on the object geometry, we project local object features into a feature-based canonical workspace. This unified spatial representation effectively decouples the functional intent from the specific object identity. Within this workspace, we learn a latent manifold (center) that models the intrinsic contact capabilities of the gripper, enabling zero-shot generalization to diverse target geometries.
Figure 2 : CoToGrasp Method Overview. The proposed framework operates in two distinct phases. Top (Object-Agnostic Training): The model learns an intrinsic, gripper-centric contact manifold within a canonical feature-based workspace, independent of object geometry. Bottom (Grasp Synthesis): At inference, a target object is transformed into the canonical frame. The network’s contact-topology-conditioned prediction is strictly filtered through a validation pipeline before energy-based optimization aligns the gripper to yield the final stable grasp ( ).
Figure 3 : Semantic Grasp Taxonomy and Contact Mapping. (Left) Correspondences between the classical Feix [ 8 ] (F) taxonomy (top), the haptic Gonzalez [ 9 ] (M) taxonomy (middle row) and our derived point cloud contact templates (bottom row). We categorized the 21 templates into three distinct functional groups: Precision , Power and Object-Specific (highly constrained topologies tailored for specific tool use). (Right) The 22 anatomical contact zones defined by Gonzalez (top) and the direct surjective mapping ( ) onto our discrete gripper handprint (bottom).
| Method | Object-Agnostic Training | SR | Speed (sec. / grasps) | Diversity (avg.) | |||
|---|---|---|---|---|---|---|---|
| (m) | R (rad) | Q (rad) | |||||
| DFC [ 23 ] | ✓ | 72.15 | 0.7389 | >1800 | 0.0607 | 1.424 | 0.3579 |
| GenDexGrasp [ 21 ] | ✗ | 71.15 | 0.5956 | 14.65 | 0.0519 | 1.416 | 0.2567 |
| DRO-Grasp [ 38 ] | ✗ | 63.30 | 0.6504 | 1.72 | 0.0546 | 1.515 | 0.2892 |
| GOAG [ 28 ] | ✓ | 77.90 | 0.6527 | 0.20 | 0.0479 | 1.401 | 0.3170 |
| CoToGrasp | ✓ | 36.94 | 0.83 | 0.11 | 0.0674 | 1.4927 | 0.3458 |
Table 1 : Comparison with taxonomy-unaware baselines. CoToGrasp achieves the highest semantic entropy ( ) and generation speed, overcoming the functional mode collapse typical of unconditioned planners.
Figure 4 : Functional contact topology distribution across taxonomy-unaware planners. The histogram illustrates the distribution of grasps generated by unconditioned baselines compared to CoToGrasp on the Multidex objects set. The unknown category represents physically stable grasps with unnatural contact patterns that fail to match any contact topology. Notably, unconditioned baselines exhibit a severe generative bias (mode collapse) toward enveloping power grasps (red box).
| Method | SR (%) | TC (%) | ||||||
|---|---|---|---|---|---|---|---|---|
| Power | Precision | Obj. Spe. | Avg. Topo. | Avg. Obj. | ||||
| Dexonomy [ 4 ] | 27.16 | 12.36 | 19.62 | 21.13 | 23.80 | 0.91 | 14.28 | 0.77 |
| CoToGrasp | 29.75 | 22.71 | 25.50 | 26.72 | 27.56 | 0.96 | 17.18 | 0.84 |
| CoToGrasp (w/o Label-Consistency) | 25.11 | 14.77 | 20.85 | 21.14 | 22.97 | 0.94 | 14.45 | 0.81 |
| CoToGrasp (w/o Force-Closure) | 26.90 | 14.87 | 21.08 | 22.08 | 23.65 | 0.95 | 16.26 | 0.81 |
| CoToGrasp (No Check) | 25.09 | 14.73 | 20.58 | 21.06 | 23.00 | 0.94 | 14.72 | 0.81 |
Table 2 : Taxonomy-aware grasp synthesis. CoToGrasp outperforms the baseline in both physical stability (SR) and semantic accuracy (TC), particularly on highly constrained precision grasps.
| Method | SR (%) | |||||
|---|---|---|---|---|---|---|
| M2 | M6 | M11 | M13 | M18 | M21 | |
| Dexonomy [ 4 ] | 10.5 | 15.2 | 60.5 † | 20.3 | 29.6 | 37.2 † |
| CoToGrasp | 30.3 | 21.7 | 29.8 | 29.6 | 31.3 | 33.5 |
Table 3 : Per-Topology results: 3 Power grasps (M13, M18, M21), 2 Precision (M2, M6) and 1 Object-Specific (M11). † Indicates artificially inflated scores due to mode collapse, where Dexonomy defaults to unverified enveloping grasps.
Figure 5 : Real-World kinematic validation. CoToGrasp synthesizes diverse, topology-compliant grasps that are physically executable on a physical Allegro Hand using YCB [ 3 ] objects. The target contact topologies (indicated above each frame) demonstrate the physical viability of the generated grasps across both precision and power categories.
Appendix figures & tables12 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 6 : Handprint areas and workspace constitution. Left: Discretized handprints of the Shadow and Allegro hands, illustrating the manually defined anatomical zone divisions. Right: A three-quarter view of the Shadow Hand’s workspace.
Figure 7 : Taxonomy transfer mapping for anthropomorphic grippers. The handprints of the Shadow Hand (left) and the Allegro Hand (right) are segmented into manually defined, corresponding anatomical zones ( – ).
| Module / Parameter | Notation | Value / Size |
| Training & Optimization | ||
| Batch Size | – | 32 |
| Learning Rate | – | |
| Training Epochs | – | 50 |
| KLD Regularization Weight | ||
| Attention Factor | ||
Table 4 : Implementation Details and Network Hyperparameters for the CoToGrasp framework.
| Raw DGCNN | Workspace | + Attn. | |
|---|---|---|---|
| Matched Pairs | 0.35 | 0.61 | 0.66 |
| Random Pairs | 0.23 | 0.33 | 0.27 |
Table 5 : Feature Alignment (Cosine Similarity).
Figure 8 : t-SNE visualization of features after canonical workspace projection.
Figure 9 : Topology-Conditioned Pose Sampling. For specific grasp topologies (such as M4 and M12), the initial object pose is sampled within a restricted kinematic region. Middle: The template’s full active sub-workspace is shown in light blue, while the truncated sub-workspace – filtered for reachability and palm clearance – is highlighted in red. Right: Examples of the initialized object point cloud (green) successfully placed within this feasible region after the sampled spatial transformation.
| Label | Eval. | TC (%) | ||
| Consistency | Isaac | |||
| ✓ | ✓ | 0.96 | 17.18 | 0.84 |
| ✓ | ✗ | 21.92 | 0.53 | |
| ✗ | ✓ | 0.94 | 14.45 | 0.81 |
| ✗ | ✗ | 19.34 | 0.50 |
Table 6 : Topology compliance ablation.
Figure 10 : Illustration of metric-induced misclassification. While CoToGrasp strictly respects the target contact topology, the kinematic optimization may result in incidental contacts where an adjacent phalanx rests against the object surface. Despite the repulsive term in our optimization energy function, these phalanges often cannot be pushed away due to inherent kinematic constraints. For instance, the left grasp illustrates an intended M3 pinch reclassified as M12, while the right shows an M4 grasp reclassified as M15. Although the intended contacts are successfully achieved and the grasps remain physically stable, these incidental contacts trigger a strict reclassification by our automated pipeline.
Figure 11 : Histogram comparing the frequencies of effective topologies and attempted topologies (as defined Sec. 4.2 ) among all stable grasps generated by CoToGrasp (top) and Dexonomy [ 4 ] (bottom).
| Object Complexity | Metric | CoToGrasp | Dexonomy [ 4 ] |
|---|---|---|---|
| Convex Non-Convex | SR Retained | 58.72% | 37.42% |
| Severe Concavities ( , data) | SR | 18.30% | 12.05% |
| TC | 19.17% | 8.94% |
Table 7: Object-Level Analysis. Evaluating performance across geometric complexity ( ). CoToGrasp shows superior retention of both physical stability (SR) and semantic compliance (TC) on challenging non-convex objects.
Figure 12 : Qualitative Synthesis Gallery on the Allegro Hand. Synthesized grasp configurations for a diverse subset of the YCB object dataset.
Figure 13 : Topological Clustering of Shadow Hand Grasps. Grasps grouped by contact topology (M1-M21), demonstrating consistent semantic alignment across varied object classes.