DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation
1. Paper overview
In one sentence: DexGraspNet scales geometry-based grasp optimization and simulation filtering into useful training data, while leaving precision grasping and the balance between pose diversity and stability unresolved. e02e05e06e07e11e12e14
| At a glance | What to know |
|---|---|
| Research problem | Source description High-dimensional robotic hands need varied grasp supervision. Existing GraspIt!-generated DDGdata is small, and its low-dimensional search concentrates fingers into restricted motions. DexGraspNet targets scalable, diverse static grasp synthesis from object geometry; the evaluated task is simulated grasp holding, not a complete manipulation trajectory. e02e07e09 |
| Core mechanism | Source description The dataset contains 1.32 million validated grasps, with more than 200 per object. Of 5,355 objects, 3,980 CAD models have 133 category labels; 1,375 scanned objects remain unlabeled. Thus the abstract's 'more than 133' categories should not be read as a complete labeled taxonomy. e02e03 |
| A key reported result | DDG grasp quality across the two reported test sets: DexGraspNet-trained: 67.5 / 0.0582 / 0.173 on DexGraspNet; 75.9 / 0.0524 / 0.134 on DDGdata. Success (%); mean Q1; penetration (cm). DDG trained on DexGraspNet or DDGdata; evaluated on each named dataset. Exact test membership and split sizes are not provided. Success requires at least one gravity direction and penetration below 5 mm. DDGdata-trained: 57.4 / 0.0493 / 0.353 and 56.4 / 0.0461 / 0.333, respectively. Success increases by 10.1 and 19.5 percentage points under the reported protocols. These are simulated benchmark results, not six-direction dataset-validation rates or physical deployment success. e07e11e12e15 |
| Reading caution | Source description The authors identify a bias toward contact-rich power grasps: the attraction objective makes precision grasps rare. Missing semantic guidance also hinders functional choices such as grasping a mug by its handle. e14 |
Core contributions
- Source description
The dataset contains 1.32 million validated grasps, with more than 200 per object. Of 5,355 objects, 3,980 CAD models have 133 category labels; 1,375 scanned objects remain unlabeled. Thus the abstract's 'more than 133' categories should not be read as a complete labeled taxonomy. e02e03
- Source description
The synthesis pipeline combines structured initialization, reversed penetration checking, hand feasibility penalties and direct mesh distances. Its downstream evidence is a comparison of two established predictors trained on alternative datasets, not a newly proposed learned world model. e05e06e10e12
Figure 2. The generator constrains where optimization starts while preserving variation around the object. Original paper, p. 4 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Read the three panels as different geometric ingredients. Panel (a) marks the hand samples used by the self-penetration penalty. Panel (b) shows contact candidates on the canonical open hand; Section III-B specifies 140 candidates and four sampled contacts for the augmented grasp representation. Panel (c) places initial hands around the blue inflated convex hull. The text describes inflating hull vertices by 0.2 m, jittering joint angles within their limits, and orienting the palm toward the object. These are starting poses, not optimized results. There are no network branches or learned attention paths in this diagram: it explains the initialization and sampling used by a pose optimizer. e04e05e06e15
What it supports. The authors make optimization easier by opening the fingers and facing the palm toward the object before descent begins. Sampling around the object retains variation in approach location and orientation. This is the proposed mechanism behind improved convergence and yield, but the figure itself does not measure its independent contribution.
Where the evidence stops. The green spheres illustrate sampling geometry. Equation (4) penalizes sampled-point separation below δ; this should not be interpreted as an exact hand-mesh collision test. The PDF also leaves detailed jitter parameters unspecified.
2. Motivation
2.1 The problem and the proposed response
High-dimensional robotic hands need varied grasp supervision. Existing GraspIt!-generated DDGdata is small, and its low-dimensional search concentrates fingers into restricted motions. DexGraspNet targets scalable, diverse static grasp synthesis from object geometry; the evaluated task is simulated grasp holding, not a complete manipulation trajectory. e02e07e09
2.2 What this reading follows
A dexterous hand can reach many poses, but only some make stable grasps. DexGraspNet turns that search problem into a dataset: initialize hands around objects, optimize contact and feasibility energies, then keep candidates that survive stringent simulation checks. The result is 1.32 million ShadowHand grasps on 5,355 objects. This reading follows the geometry that makes generation practical, the diagnostics used to characterize the retained poses, and the experiments that test their value as supervision. Keep two distinctions in view: candidate yield differs from dataset validity, and the learned predictors are evaluated under a looser holding criterion than the one used to build the dataset. e02e05e06e07e11e12e14
3. Research context
We place the paper in the collection through its world–action interface. The catalog labels and the reading’s assessment are shown separately.
| Catalog dimension | Recorded classification |
|---|---|
| Major category | Datasets |
| Architecture | Not applicable |
| Prediction paradigm | Not applicable |
| Quadrant | Not applicable |
3.1 Evidence-based assessment
Supports the recorded classification
The catalog's dataset classification is supported by object-associated synthetic grasp annotations and downstream training comparisons. Architecture, prediction paradigm and quadrant are appropriately not applicable: the contribution is an optimization-and-validation data generator, not an integrated model predicting future observations and actions. The benchmark networks are separate users of the dataset. e03e04e06e07e10
This is the collection’s architectural analysis, not a new related-work survey. Benchmark comparisons and their protocols appear in Section 6.
4. Problem formulation
4.1 Inputs and outputs
| Inputs | Outputs |
|---|---|
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4.2 Equations and their role
5. Method in detail
5.1 1. Make the starting geometry compatible with grasp optimization
The generator starts from an object mesh and a kinematic hand rather than a learned pose prior. Translation, rotation and joint angles determine the hand surface, while temporary sampled contacts enter the force-closure objective. Figure 2 explains why initialization matters: a closed hand may give descent a poor starting configuration, so the authors choose an open reference pose and place the palm toward the object. The energy then attracts contacts, discourages penetration, and penalizes infeasible hand configurations. For thin object meshes, penetration checking changes direction: object samples are tested against the hand instead of relying on hand samples lying inside a thick object. Direct mesh distances remove the need for category-level DeepSDF training. These changes work together; the PDF does not experimentally isolate each contribution. e03e04e05e06e15
5.2 2. Follow a candidate through filtering, learning and evaluation
Optimization supplies candidate grasps, not final dataset labels by itself. The validation stage initializes a hand in each optimized pose, applies position-control targets, and tests whether the object remains in contact under every axis-aligned gravity direction. Penetration above 1 mm also rejects a candidate. The resulting object-associated poses become supervision for separate networks. DDG predicts poses from depth images after supervised training and differentiable-quality fine-tuning. GraspTTA samples a pose from an object-conditioned CVAE and refines it toward a predicted contact map at inference. Neither benchmark produces a full manipulation policy in this paper. Crucially, the predicted poses are evaluated with an easier rule: one successful gravity direction and penetration below 5 mm. A benchmark success percentage therefore cannot be read as the fraction passing the dataset's construction standard. e04e07e10e11
5.3 3. Separate supervision value from claims about general dexterity
Reader analysis: the strongest learning evidence is the within-method row comparison in Table III. Each predictor benefits from DexGraspNet training on both named test sets. That supports the dataset's value under the reported protocol, while missing split details limit conclusions about object-level generalization. Figure 6 and Table II provide a complementary argument about pose coverage: broader marginal joint distributions coexist with better geometric quality. Table IV then shows why coverage is not enough. GraspTTA produces higher entropy but far lower success than DDG. A useful follow-up would measure diversity only among accepted predictions, with the acceptance rule declared in advance. The power-grasp bias and lack of semantic guidance further mean that more poses do not automatically supply precision manipulation or task-appropriate contact choices. e09e11e12e13e14e15
5.4 Training and inference
During training
Dataset generation optimizes individual poses rather than training a grasp network. For the benchmark, DDG first learns pose supervision, then fine-tunes with differentiable Q1 and contact/penetration losses. The authors follow its preprocessing and official training settings. e06e10
GraspTTA is reimplemented for ShadowHand. Its conditional variational autoencoder and contact network are trained separately; the authors use the same number of training iterations on the two datasets. Exact iteration counts and train/test membership are not supplied here. e10e15
During inference
DDG regresses a global hand pose and joint angles from five depth images. GraspTTA conditions on an object point cloud, samples a latent code, decodes a pose, obtains a hand point cloud through kinematics, and optimizes pose parameters toward a predicted contact map. These are pose predictions/refinements; the paper evaluates their holding stability in simulation. e10e11
5.5 Implementation flow
- Prepare geometry
Collect CAD and scanned objects, normalize each into a unit sphere, apply five fixed size augmentations, remesh into manifolds, and use CoACD convex decomposition for simulation collision geometry. The five scale values are not specified in this PDF. e03
- Parameterize and initialize
A grasp is g=(T,R,θ), with 22 joint angles in the generation formulation. Forward kinematics gives the hand mesh. Select four contacts from 140 hand-surface candidates. Jitter an open reference hand, sample around the object's convex hull inflated by 0.2 m, and orient the palm toward the object. e04e05
- Optimize candidate poses
Minimize force-closure, contact-distance, object–hand penetration, self-penetration and joint-limit energies. Reverse penetration checking samples the object and measures against the hand, addressing thin object meshes. Gradient descent updates translation, rotation and joints instead of the predecessor's Langevin updates. Modified Kaolin mesh distances replace category-level DeepSDF pretraining. e06
- Validate and retain
In Isaac Gym/PhysX, offset contacting links to create position-control targets. A candidate must remain in contact after 100 simulation steps under each of six axis-aligned gravity directions at 9.8 m/s². Reject maximum penetration above 0.1 cm. Only candidates passing both tests enter the dataset. e07
6. Experiments & results
DexGraspNet supplies 1.32 million simulated ShadowHand grasps on 5,355 objects. Geometry-based optimization generates candidate poses, and stringent simulation filtering selects the dataset. Training two existing grasp predictors on it improves reported quality and diversity, but the benchmark uses a weaker success criterion than dataset construction. Precision and functional grasps remain poorly covered.
The eight-page paper has no controlled component-ablation figure or table. Figures 3 and 6 serve as diagnostics of candidate yield and pose distribution, not causal tests of individual generator changes. Figure 2 is the original method illustration; no learned architecture is proposed for the dataset generator. The reported empirical evaluations are simulations and do not include physical-robot trials. e05e06e07e08e09e10e15
6.1 Read the original evidence
Table II. The dataset comparison measures wrench-space quality and joint diversity with separate statistics. Original paper, p. 5 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Read down each column to compare datasets under the same metric. The '100%' heading means the mean over all grasps, not a perfect success rate. The next column restricts the mean to the best 10% of grasps. Q1 is defined in Equation (7) using the radius of a sphere inscribed in the convex hull of contact friction-cone wrenches. For this comparison, contacts use a 1 mm threshold and at most one contact point per hand link. The final column is mean joint entropy, computed from 100-bin distributions. Keep these quality and diversity measures separate: they answer different questions about the stored examples. e06e09e11
What it supports. DexGraspNet raises all-grasp mean Q1 from 0.0712 to 0.1145 and best-decile mean Q1 from 0.2277 to 0.2533. Mean entropy rises from 4.246 to 5.962. The authors note that neither Q1 nor entropy is explicitly optimized as the dataset-generation metric, though generation uses a related force-closure surrogate.
Where the evidence stops. The paper does not state the entropy logarithm base or report uncertainty for these values. Table III uses a relaxed 1 cm contact threshold and additional Q1 handling, so its Q1 numbers should not be directly pooled with these.
Table III. Both predictors improve when trained on DexGraspNet under the paper's benchmark protocol. Original paper, p. 6 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Within each pair of rows, hold the method fixed and change only the named training dataset; the two column groups identify the evaluation dataset. The arrows mark higher success and Q1 as desirable and lower penetration as desirable. Section V-B supplies the units absent from the header: success is percent and penetration is centimeters. It also supplies the acceptance rule: surviving at least one gravity direction with penetration below 5 mm. Q1 uses a 1 cm contact threshold, with scores set to zero when penetration exceeds 5 mm. This means the table evaluates predicted poses with more forgiving criteria than those used to accept the training dataset's grasps. e02e07e10e11e12e15
What it supports. For DDG on the DexGraspNet test set, changing training data increases success from 57.4% to 67.5% and reduces penetration from 0.353 to 0.173 cm. On DDGdata, success rises from 56.4% to 75.9%. GraspTTA also improves on every reported metric, although its success remains much lower than DDG's.
Where the evidence stops. Exact test membership, split sizes, overlap checks and confidence intervals are not supplied. The two training datasets also differ in scale and object coverage. These results support the reported training-data comparison without isolating a single causal dataset property.
Table IV. Greater output entropy must be assessed alongside the probability of producing a valid grasp. Original paper, p. 6 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Use the same paired-row reading as Table III. Within each test-dataset block, H mean summarizes joint-angle entropy and H std reports entropy dispersion; the latter is not labeled as repeated-run uncertainty. DexGraspNet training increases each mean and decreases each standard deviation. Now compare methods while keeping the training and test datasets fixed. GraspTTA's entropy is higher, but Table III shows lower grasp success. The source discusses this explicitly: an output distribution can spread across more poses while containing many unsuccessful ones. Read the two tables together before interpreting the larger entropy as a better grasp generator. e09e11e12e13e14
What it supports. On DexGraspNet, its training data gives DDG entropy 5.683 and GraspTTA entropy 6.111. Their success rates are 67.5% and 24.5%, respectively. GraspTTA even exceeds the dataset's own mean entropy of 5.962, but the authors caution that this does not establish more diverse successful grasping.
Where the evidence stops. The table does not separately report entropy restricted to valid grasps. Marginal joint entropy also leaves joint coordination and grasp semantics unresolved. A larger value can therefore coexist with greater penetration or less useful behavior.
6.2 Results and evaluation conditions
| Task & protocol | Reported result | Comparison & interpretation |
|---|---|---|
| Dataset grasp quality and diversity DexGraspNet versus DDGdata; Q1 uses a 1 mm contact threshold and at most one contact per hand link; entropy discretizes each joint range into 100 bins. | DexGraspNet: 0.1145; 0.2533; 5.962. All-grasp mean Q1; best-decile mean Q1; mean joint entropy | DDGdata: 0.0712; 0.2277; 4.246. Both geometric quality and marginal joint diversity improve. Entropy's logarithm base is unstated; these Q1 values use a different contact threshold from the learned-predictor benchmark. e09e11 |
| DDG grasp quality across the two reported test sets DDG trained on DexGraspNet or DDGdata; evaluated on each named dataset. Exact test membership and split sizes are not provided. Success requires at least one gravity direction and penetration below 5 mm. | DexGraspNet-trained: 67.5 / 0.0582 / 0.173 on DexGraspNet; 75.9 / 0.0524 / 0.134 on DDGdata. Success (%); mean Q1; penetration (cm) | DDGdata-trained: 57.4 / 0.0493 / 0.353 and 56.4 / 0.0461 / 0.333, respectively. Success increases by 10.1 and 19.5 percentage points under the reported protocols. These are simulated benchmark results, not six-direction dataset-validation rates or physical deployment success. e07e11e12e15 |
| GraspTTA quality and diversity tradeoff ShadowHand reimplementation on the same two reported test sets and relaxed benchmark validation. | DexGraspNet-trained: 24.5 / 0.0271 / 0.678 on DexGraspNet; 39.3 / 0.0790 / 0.547 on DDGdata. Corresponding entropies: 6.111 and 5.947. Success (%); mean Q1; penetration (cm); mean joint entropy | DDGdata-trained: 17.1 / 0.0126 / 0.720 and 23.7 / 0.0265 / 0.666; entropies 5.952 and 5.837. Training-data improvements recur, but high pose entropy coexists with low success. On DexGraspNet, the DexGraspNet-trained DDG has entropy 5.683 and substantially greater success; diversity alone does not rank useful grasps. e10e11e12e13 |
| Large-scale grasp generation cost Authors' complete pipeline on A100 versus predecessor on RTX 3090, with different batch sizes and iteration counts. | Reported: 74 minutes for 10,000 candidates over 6,000 steps, approximately 18% valid; 950 A100 GPU-hours for 1.32 million retained grasps. Runtime and candidate yield | Predecessor: 37 minutes for 512 candidates over 10,000 steps, approximately 3% valid. Its 50,000 GPU-hours for the full dataset is an author projection. The comparison supports practical throughput improvement but cannot isolate software changes from hardware, batching, convergence and yield effects. e08 |
6.3 Ablations and diagnostic examples
Read component removals and qualitative examples within their stated evaluation conditions.
Figure 3. A successful dataset can be built from a generator that rejects most candidate grasps. Original paper, p. 4 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. The horizontal axis is each object's average candidate success rate, expressed as a fraction; the vertical axis counts objects in each range. Read this as a distribution of generation difficulty across objects. The validation described beside the figure uses Isaac Gym with PhysX: apply position-control targets, expose the grasp to all six axis-aligned gravity directions, and check contact after 100 steps. The gravity magnitude is 9.8 m/s², and maximum penetration above 0.1 cm also causes rejection. The caption says only successful grasps are retained. Thus a low bar position along the horizontal axis indicates costly candidate generation, not a low fraction of valid examples in the released dataset. e02e07e08e11e15
What it supports. Many objects lie around the lower success-rate ranges. The accompanying throughput account reports approximately 18% valid candidates for the authors' generation setting. Filtering is therefore central to obtaining the final 1.32 million grasps; faster optimization alone does not describe how efficiently accepted examples are produced.
Where the evidence stops. This is a diagnostic histogram, not an initialization ablation. It neither isolates the reason an object is difficult nor establishes physical-robot reliability. Its strict criterion differs from the learned-predictor benchmark on page 6.
Figure 6. Joint-level diagnostics reveal how the two datasets occupy different parts of pose space. Original paper, p. 5 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Start with the color key in the lower plot: blue denotes DexGraspNet and red denotes DDGdata. The hand overlay at left illustrates the restricted fan of finger poses attributed to GraspIt!'s search, compared with a broader spread for DexGraspNet. The two plots show probability distributions for two joint angles, with prominent red concentrations and more distributed blue mass. These are selected marginal views, not a full distribution over coordinated hand configurations. Section IV then generalizes the comparison by discretizing each joint's motion range into 100 bins, estimating its distribution, and averaging entropy over joints. Table II reports that aggregate statistic separately. e02e09e14
What it supports. The plots support the paper's diagnosis that DDGdata often concentrates joint angles near limits, while DexGraspNet explores more of the joint ranges. The associated mean entropy is 5.962 versus 4.246. This is evidence of broader joint-pose coverage, not evidence that every grasp type or functional intent is represented.
Where the evidence stops. The figure does not identify the two joints or label angle units. Marginal distributions cannot establish diversity of coordinated successful grasps. The authors explicitly report missing precision grasps and difficulty generating functional grasps.
7. Analysis & limitations
7.1 What the evidence leaves open
The authors identify a bias toward contact-rich power grasps: the attraction objective makes precision grasps rare. Missing semantic guidance also hinders functional choices such as grasping a mug by its handle. e14
There is no controlled component ablation or physical-robot experiment in this PDF. Dataset size, object coverage and generation strategy change together, so benchmark gains do not isolate which dataset property causes improvement. Confidence intervals and test-overlap checks are not reported. e02e08e12e15
Table I lists DDGdata as 6.9k grasps on 565 objects while giving '>100' grasps per object. These entries are not mutually consistent as dataset-wide averages; the per-object entry is not used here to infer coverage. e16
7.2 Questions for discussion
- How much of the dataset advantage survives matched object coverage and grasp counts?
- Can precision and functional grasps be added without sacrificing stability?
- Does the entropy ranking survive when computed only over grasps passing the strict validation criterion?
8. Reproducibility audit
8.1 Requirements and known gaps
Reconstruction requires source object meshes, the hand URDF and samples, manifold remeshing, CoACD, modified Kaolin, and Isaac Gym/PhysX. The PDF omits exact scale values, jitter distributions' parameters, full optimizer settings, physics/contact settings and software versions. Its project link is an access pointer; dataset licensing is not specified in this text. e01e03e05e06e07e15
Proposed checks: isolate initialization on fixed geometry and hardware, measuring validated grasps per GPU-hour; then reevaluate identical predicted poses under both published gravity/penetration criteria, measuring success and diversity among valid grasps. Neither check is reported as performed here. e05e07e08e11e13
8.2 Proposed reproduction checks
The following checks are proposals motivated by the paper. They have not been run as part of this reading.
Check 1: Isolate the contribution of structured initialization
Reader-proposed, not performed: use a fixed object subset spanning CAD and scanned models, identical scales, hand samples, final energy, optimizer, batch size and GPU. Compare the published open, object-facing initialization against random pose initialization, with repeated seeds and the same iteration budget. Record time to convergence, strict six-direction/1 mm acceptance, accepted grasps per GPU-hour, and entropy among accepted grasps. The mechanism predicts better accepted-grasp throughput for structured initialization; disappearance of the advantage under these controls would weaken the initialization-specific explanation. This deliberately removes the hardware and batching differences in the published predecessor comparison. e03e05e06e07e08e09e15
Check 2: Measure how the validation rule changes quality and diversity rankings
Reader-proposed, not performed: freeze predicted poses from both methods and both training datasets on one explicit object-disjoint test list. Using identical physics settings, evaluate a two-by-two grid: at least one versus all six gravity directions, and penetration below 5 mm versus at most 1 mm. Change no predictions between cells. Report success, rejection causes, and 100-bin joint entropy both before and after filtering, with repeated-seed uncertainty. If GraspTTA's entropy advantage vanishes among strict-pass grasps, its high unconditional entropy would be explained partly by failures. Publish the test identifiers because the PDF does not supply them. e07e09e10e11e12e13e15
8.3 Reading coverage
Visual audit: The title, authors, affiliations and arXiv v2 stamp were inspected on page 1. Page 2 supplied Table I and the background comparison. Page 3 supplied object preparation, grasp notation, Equation (1) and initialization details. Page 4 supplied Figure 2, Equations (4)–(6), validation, Figures 3–4 and the start of the runtime account. Page 5 supplied Figures 5–6, Table II, metric definitions, the remaining runtime account and benchmark training descriptions. Page 6 supplied Tables III–IV, inference details, evaluation thresholds and limitations. All six final original crops were separately inspected for legibility and completeness. Figure 2's panel roles were checked against its caption and Equations (4)–(6); table metric directions and units were checked against the body. The reference-only pages 7–8 were read as text. No separate supplement was supplied, and no code or experiments were inspected or executed.
PDF pages inspected for this edition: 1, 2, 3, 4, 5, 6. Appendix coverage: not present.
Original figures and tables remain the work of the source’s authors. Extractions preserve their scientific content; any HTML wrapper layout is disclosed with each figure. The surrounding reading notes are our own.
Text reading scope & known omissions
- Abstract and I. Introduction (p. 1–2)
- II. Related Work, A–C (pp. 2–3)
- III. Dataset Generation Method, A–C (pp. 3–4)
- IV. Dataset Analysis and Comparison (pp. 4–5)
- V. Benchmarks, A–B (pp. 5–6)
- VI. Limitations; VII. Conclusions; VIII. Acknowledgements (p. 6)
- References [1]–[65] (pp. 7–8)
Outside the original text pass
- Text extraction does not reconstruct figure images; inspect the retained PDF for figures and equation/table layout.
- Separate supplemental material availability has not been fully verified.
- The verified artifact is arXiv:2210.02697v2, dated 8 March 2023. Its title and seven authors match the catalog. The catalog lists ICRA 2023; neither the proceedings edition nor earlier revisions were supplied, so their differences from this revision cannot be established.
- Acquisition omission preserved: text extraction does not reconstruct figure images. This was addressed by inspecting the retained PDF on pages 1–6 and all six final crops.
- Separate supplemental material availability has not been fully verified; no separate supplement was supplied. No appendix occurs in the eight-page PDF.
- References on pages 7–8 were read as text, without a separate visual inspection. Code, dataset files and linked websites were not inspected; no experiments were reproduced.
The visual audit above records the subsequent illustrated pass.
8.4 Traceable evidence
e01PDF p. 1, title block, affiliations, arXiv margin stamp and abstract
The exact catalog title and seven authors appear in order: Ruicheng Wang, Jialiang Zhang, Jiayi Chen, Yinzhen Xu, Puhao Li, Tengyu Liu, He Wang. Affiliations are Peking University; Beijing Institute for General Artificial Intelligence; Tsinghua University. The stamp identifies arXiv:2210.02697v2, 8 March 2023. The abstract points to a project page for data and code.
Go to primary source ↓e02PDF pp. 1–2, Abstract, Section I and Table I; p. 2, Section II-C
DexGraspNet reports 1.32M ShadowHand grasps on 5355 objects and more than 200 grasps per object. The introduction motivates larger and more diverse supervision; Section II-C describes GraspIt!'s eigengrasp search and restricted pose distribution.
Go to primary source ↓e03PDF p. 3, Section III-A, Object Preperation
3980 ShapeNet CAD objects cover 133 categories; 1375 objects from YCB, BigBIRD, Grasp, KIT and Google scanned repositories are unlabeled. Models are normalized into a unit sphere, augmented at five unspecified fixed sizes, remeshed into manifolds, and convex-decomposed with CoACD.
Go to primary source ↓e04PDF p. 3, Section III-B.1–2, grasp notation and Equation (1)
The formulation defines g=(T,R,θ), with d=22 for ShadowHand, URDF forward kinematics, 140 manually selected hand contacts and n=4 sampled contact points. Contact variables are temporary. Equation (1) is the norm ||Gc||₂; G uses identity and contact cross-product blocks.
Go to primary source ↓e05PDF p. 3, Section III-B.3 initialization paragraphs; p. 4, Figure 2 and caption
The open canonical hand is jittered within joint limits. Sampling uses the object convex hull inflated by 0.2 m and an object-facing palm. Figure 2 distinguishes self-penetration spheres, contact candidates, and surrounding initial poses; its caption gives δ=1 cm for the spheres.
Go to primary source ↓e06PDF pp. 3–4, Section III-B.2–3, Equations (1)–(6) and optimizer/distance paragraphs
The revised energy adds self-penetration and joint-limit penalties with weights 100,100,10,1 for distance, penetration, self-penetration and joint limits. Reversed penetration samples objects against hand geometry. The authors replace Langevin pose updates with gradient descent and DeepSDF distances with modified Kaolin; convergence is described as below 6000 iterations.
Go to primary source ↓e07PDF p. 4, Section III-C and Figure 3/caption
Isaac Gym with PhysX tests position-controlled final grasps under all six axis-aligned gravity directions, at magnitude 9.8 m/s² for 100 steps. Penetration above 0.1 cm fails. Figure 3 plots the number of objects against average candidate success rate; only successful grasps are saved.
Go to primary source ↓e08PDF p. 4, Section IV final paragraph continuing on p. 5, first left-column paragraph below Figure 6
The authors report A100 runtime of 74 minutes for 10000 candidates and 6000 steps with about 18% validity; predecessor RTX 3090 runtime is 37 minutes for 512 candidates and 10000 steps with about 3% validity. Full generation consumed 950 A100 GPU-hours; 50000 predecessor GPU-hours is counterfactual projected cost.
Go to primary source ↓e09PDF p. 5, Figure 6/caption, Section IV diversity and Q1 paragraphs, Equation (7), Table II both rows
Figure 6 contrasts blue DexGraspNet joint distributions with red DDGdata concentration. Entropy uses 100 bins per joint and averages over joints; no logarithm base is given. Q1 is the inscribed-sphere radius of the contact-wrench convex hull, using 1 mm contacts and at most one contact per link. Table II values are DDGdata 0.0712/0.2277/4.246 and DexGraspNet 0.1145/0.2533/5.962.
Go to primary source ↓e10PDF pp. 5–6, Section V-A, Benchmark Methods
DDG uses five depth views and two-stage pose then differentiable-Q1 training, following official settings. GraspTTA separately trains a CVAE and contact network, then predicts and optimizes a pose toward contact consistency at test time. Its ShadowHand reimplementation is trained for equal iteration counts on each dataset.
Go to primary source ↓e11PDF p. 6, Section V-B first paragraph, evaluation metrics 1–3
Benchmark success requires one or more gravity directions and penetration below 5 mm. Q1 uses a relaxed 1 cm contact threshold and is set to zero for penetration above 5 mm. Penetration is measured from object points to hand meshes and reported in centimeters.
Go to primary source ↓e12PDF p. 6, Table III, all four rows and both test-dataset column groups
DDG's DexGraspNet-test success/Q1/penetration changes from 57.4/0.0493/0.353 to 67.5/0.0582/0.173 with DexGraspNet training; its DDGdata-test values change from 56.4/0.0461/0.333 to 75.9/0.0524/0.134. GraspTTA changes from 17.1/0.0126/0.720 to 24.5/0.0271/0.678 and from 23.7/0.0265/0.666 to 39.3/0.0790/0.547, respectively.
Go to primary source ↓e13PDF p. 6, Table IV, all rows; Section V-B diversity discussion
On DexGraspNet/DDGdata tests, DDG mean entropy rises from 4.958/3.709 to 5.683/4.272 with DexGraspNet training; GraspTTA rises from 5.952/5.837 to 6.111/5.947. Reported entropy standard deviations also decrease. The authors caution that GraspTTA's high entropy does not establish diverse successful grasping because success is low.
Go to primary source ↓e14PDF p. 6, Section VI, Limitations
Attracting candidate contacts toward objects biases generated grasps toward power grasps, with precision grasps hardly appearing. Lack of semantic guidance makes functional grasps, such as using a mug handle, difficult.
Go to primary source ↓e15PDF pp. 3–6, Sections III–V, generation configuration and benchmark protocol
The PDF specifies selected geometry, energy and validation settings but does not supply scale values, full jitter/optimizer parameters, simulator contact parameters or software versions. Benchmarks refer to official settings without exact split membership, iteration counts, overlap checks or confidence intervals. Reported studies are simulation comparisons, not controlled component ablations or physical-robot trials; no dataset license is stated.
Go to primary source ↓e16PDF p. 2, Table I, DDGdata row, Grasps / Obj.(Cat.) / Grasps per Obj. columns
The original row displays 6.9k, 565 and >100 respectively. Their incompatibility as a dataset-wide average is a reader-observed table inconsistency, not a corrected source statistic.
Go to primary source ↓8.5 Primary sources
DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation ↗
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