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GOAG: Generative and Object-Agnostic Grasp Planner for Dexterous Robotic Manipulation

Julien Merand, Boris Meden, Mathieu Grossard, Liming Chen

arXiv:2608.19759v1cs.ROcs.AI

TL;DR

Dexterous grasp planners trained on restricted object sets can struggle to generalize to novel shapes. GOAG learns gripper contact priors without object geometry and introduces object features at inference, achieving the second-highest average success rate across five benchmarks with one object-agnostic Shadow Hand model.

  • Problem

    Data-driven grasp planners are often evaluated on restricted object sets, limiting evidence of their generalization to novel, real-world shapes.

    Source: §I · p. 1

  • Method

    GOAG trains a generative model on a gripper’s geometry and kinematics, then uses object features at inference to identify compatible contact areas.

    Source: §I · p. 1

  • Results

    Across five benchmarks, GOAG achieved the second-highest average success rate using one object-agnostic Shadow Hand model, while competitors were trained on each dataset.

    Source: E. Overall Performances · p. 7

  • Takeaways & Limitations

    GOAG achieved performance competitive with state-of-the-art approaches without training on their specific datasets and was validated in a successful real-robot deployment.

    Source: §V · p. 8

    GOAG’s simple pose-sampling method is less effective for larger objects, for which the authors suggest using a more advanced sampling strategy.

    Source: E. Overall Performances · p. 7

Abstract

from arXiv · show

Multifingered grasping is a crucial robotic skill, but current deep-learning grasp planners often struggle to generalize to new objects because they are trained on limited, object-specific datasets. We introduce a fundamentally different approach, grounded in the observation that the gripper and the object share identical surface geometry at their mutual contact points. We propose GOAG: Generative and Object-Agnostic Grasp Planner for Dexterous Robotic Manipulation, a novel deep generative model that learns a compact latent representation of a specific gripper's contact surface distribution, enabling the efficient sampling of valid grasp configurations without relying on object-specific training data. We show that by introducing object features only at inference time, our model can effectively retrieve admissible contact areas that are compatible with the gripper's capabilities. We validate our approach through extensive experiments on established grasp protocols in both simulated and real-world scenarios, demonstrating its effectiveness with different grippers from the literature. Our method delivers state-of-the-art results on the objects from the MultiDex dataset, achieving an average success rate of 86.93%. It offers significantly faster processing when generating numerous grasps, while matching the performance of leading approaches specifically trained on this dataset. Unlike these methods, our approach does not rely on object-specific training data, highlighting the advantages of object-agnostic learning. It effectively addresses the generalization challenges faced by traditional data-driven grasp planners. Code and videos are available on our project website https://cea-list.github.io/goagweb/ .

I. INTRODUCTION

GOAG shifts dexterous grasp planning to a gripper-centric approach: it learns feasible contact areas from gripper geometry and kinematics without object data, then introduces object features at inference. The method targets the generalization limits of object-specific data-driven planners while avoiding the time constraints of traditional analytical methods.

I. INTRODUCTION

  • Traditional analytical planners struggle with real-time constraints, while data-driven planners often generalize poorly beyond restricted object sets.The introduction frames these limitations as barriers to using dexterous hands on novel real-world shapes.Source: §I · p. 1
  • GOAG trains without object geometry, using the shared contact zones of object and gripper surfaces to learn a gripper-centric grasping strategy.The model captures possible contact areas for a specific gripper before encountering an object.Source: §I · p. 1, Figure 1 · p. 1
  • A synthetic dataset maps grasp-taxonomy labels to random kinematic configurations, and a CVAE learns the conditional distribution of feasible contact areas from gripper geometry and kinematics.This object-agnostic training setup is intended to support zero-shot generalization to arbitrary object shapes.Source: §I · p. 1
  • At inference, a BPS encoding of the object guides contact-area retrieval; PointNet++ [16] maps contacts to gripper links, after which joint optimization enables grasp execution.Object features are introduced only at inference time.Source: §I · pp. 1–2
  • The authors validate GOAG in simulation and real-world experiments, following established protocols and extending tests to novel objects to assess generalization.The introduction reports validation scope but gives no quantitative results here.Source: §I · p. 2

II. RELATED WORK · A. Grasp Databases · B. Learning Explicit Grasps

Dexterous grasp planning has increasingly relied on data-driven methods, whose performance depends on grasp databases that can constrain generalization. Explicit-grasp methods span direct prediction and generative models, but may require costly filtering to enforce physical validity.

II. RELATED WORK

  • Recent advances in dexterous grasp planning have been largely driven by data-driven approaches.Table I summarizes their characteristics.Source: §II · p. 2

A. Grasp Databases

  • Early grasp datasets used human demonstrations, motion capture, teleoperation, or thermal imaging to collect grasps –.Source: A. Grasp Databases · p. 2
  • Recent datasets automate generation of thousands of physically validated candidate grasps using analytical planners and simulators such as Isaac Gym,,,, –.Source: A. Grasp Databases · p. 2
  • Grasp databases are costly to generate, and their gripper–object-specific mappings bias trained models toward training geometry, hindering generalization to unseen shapes.Source: A. Grasp Databases · p. 2

B. Learning Explicit Grasps

  • Explicit-grasp methods map object representations to complete grasp configurations, evolving from regression [27], to conditional generative models,, diffusion,,, and foundation models,,.Source: B. Learning Explicit Grasps · p. 2
  • GOAG adapts a human grasp taxonomy to Allegro Hand geometry and samples contacts only within admissible regions, learning feasible contact distributions from gripper kinematics independently of objects.Source: Figure 2 · p. 2
  • Direct-prediction methods may generate poses that violate physical constraints, so they often require costly post-hoc validation or a learned discriminator to remove penetrating or unstable grasps.Source: B. Learning Explicit Grasps · p. 2

C. Learning Intermediate Representation · III. METHOD

GOAG represents contact regions as an intrinsic property of the gripper, training on gripper geometry and using object geometry only at inference. Its pipeline generates and refines candidate grasps from predicted contact points.

C. Learning Intermediate Representation

  • Prior intermediate-representation methods use object-surface contact maps,,, or gripper-specific features,,, but still rely on object-grasp databases.GOAG instead models gripper contact capabilities independently of object data and precomputed grasp examples.Source: C. Learning Intermediate Representation · p. 2

III. METHOD

  • GOAG trains using gripper geometry alone and uses the targeted object geometry only during inference.This shifts contact-region modeling from the object to an intrinsic property of the gripper.Source: §III · pp. 2–3
  • During training, a CVAE reconstructs gripper contact distributions encoded with a workspace-tied Basis Point Set, while PointNet++ maps contact points to gripper links.The training process samples gripper configurations to produce handprints and corresponding contact points.Source: Figure 3 · p. 3
  • At inference, the object is encoded in the inverse gripper pose, and latent-variable sampling generates diverse plausible contact points that the Links Mapper assigns to gripper links.This enables grasp generation for a novel object without object-specific training data.Source: Figure 3 · p. 3
  • A force-closure check tests predicted contacts for stability before grasp optimization refines the final gripper configuration.Source: Figure 3 · p. 3
  • GOAG defines the gripper handprint as surface points on active grasping areas, including the palm and inward-facing link surfaces.A grasp combines the gripper pose [R, T] with joint values Q.Source: §III · p. 3

A. Shifting to a Gripper-Oriented Paradigm

GOAG reframes grasp contacts in the gripper’s canonical frame, learning feasible contact priors from gripper geometry and constraints rather than object-specific data. These priors can be queried against object shapes at inference time.

A. Shifting to a Gripper-Oriented Paradigm

  • The conventional definition places contact points on the object surface when they fall within an aligned-distance threshold of the posed gripper.The gripper handprint is positioned by grasp pose and joint configuration.Source: A. Shifting to a Gripper-Oriented… · p. 3
  • Applying the inverse grasp-pose transform to the object moves analysis into the gripper’s canonical frame, where contacts are defined on the gripper surface.The gripper is in its default pose and joint configuration, while the object is transformed into the gripper’s frame.Source: A. Shifting to a Gripper-Oriented… · p. 3
  • Feasible contact zones can be learned as object-independent priors from gripper kinematics and grasp taxonomy, precomputed across configurations, and queried against object shapes at inference.The set of kinematically feasible contact patterns is bounded by the gripper’s geometry and constraints.Source: A. Shifting to a Gripper-Oriented… · pp. 3–4

B. Training GOAG from C(H(Q))

GOAG trains on kinematically feasible gripper contact patterns rather than object-specific data, using a BPS representation to learn contact distributions and point-to-link assignments. At inference, the same representation accepts object point clouds, creating a train-test geometry shift.

Contact Data Generation

  • Taxonomy-guided sampling of valid joint configurations and admissible contact regions produces diverse, structurally valid training targets independent of object geometry.Active contact points are sampled within the admissible regions to encourage realistic, kinematically harmonious grasps.Source: B. Training GOAG from C(H(Q)) · p. 4

BPS Encoding and Distance Field

  • The workspace BPS encodes gripper training clouds and object inference clouds as fixed-length distance fields, creating a train-test geometry shift [15, 8].The basis points discretize the gripper’s reachable workspace relative to its palm frame; cross-domain transfer is explored further in.Source: B. Training GOAG from C(H(Q)) · p. 4
  • Projected contact values measure each basis point’s proximity and alignment to active contacts, approaching 1 for nearby aligned points and 0 for distant or misaligned points.This continuous contact-likelihood field supplies supervision alongside the distance field.Source: B. Training GOAG from C(H(Q)) · p. 4

CVAE Architecture and Training

  • The CVAE encodes distance and contact fields into a posterior latent, then decodes the latent with the distance field to predict contacts.Training combines reconstruction with a β-weighted KL divergence to N(0, I); attention-weighted L2 reconstruction prioritizes high-likelihood contact regions.Source: B. Training GOAG from C(H(Q)) · p. 4

Links Mapper

  • A parallel PointNet++ links sampled contact points to gripper phalanx links, providing the kinematic assignments needed to recover joint configurations during downstream optimization [16].The architecture is explored further in.Source: B. Training GOAG from C(H(Q)) · p. 4

C. GOAG Inference on O([R, T]−1)

At inference, GOAG samples contact maps for a novel object in the gripper’s canonical workspace, screens their stability, and optimizes a collision-avoiding joint configuration for execution.

Contact Points Force-Closure Estimation

  • For unseen objects, the trained model predicts contact points from a sampled latent prior and aligned distance field, then estimates force closure and optimizes gripper kinematics to refine grasp execution.Source: C. GOAG Inference on O([R, T]−1) · p. 4
  • Stability is tested by checking whether the wrench-space convex hull contains the origin strictly, using phalanx barycenters projected onto the surface under µ=0.3 friction.Gravity and object weight are ignored; failed samples trigger latent resampling, capped at 20 iterations.Source: C. GOAG Inference on O([R, T]−1) · p. 5

Grasp Generation

  • The final joint configuration minimizes contact-to-link distance while penalizing object and self-penetration and enforcing hardware joint limits.Weights are λd=1.0, λp=50.0, λs=0.1, and λj=1.0; self-penetration uses ϵ=0.025 m.Source: C. GOAG Inference on O([R, T]−1) · p. 5

IV. EXPERIMENTS · A. Implementation Details

GOAG’s implementation builds a large labeled contact-point dataset from sampled gripper configurations and trains a CVAE with specified workspace and network settings. The experiments also report low dataset-generation time and evaluate grasp performance across three dexterous grippers on the MultiDex test set.

A. Implementation Details

  • 3,000,000 labeled point clouds were generated from 10,000 kinematically valid configurations, with 50 admissible contact-point sets sampled per configuration and grasp type.The sampled point clouds cover the gripper’s reachable workspace.Source: A. Implementation Details · p. 5
  • 1 GPU hour on an Nvidia RTX 4090 was required for dataset creation, compared with 1,400 GPU hours on Nvidia A100 reported by.Source: A. Implementation Details · pp. 5–6
  • Active point clouds were extracted by thresholding the dot product of each mesh-vertex normal and the palm-facing direction to isolate contact pads and finger sides.Source: A. Implementation Details · p. 5
  • Six common grasp types were selected from the taxonomy adapted from; the cited literature reports these types account for 92.5% of machinists’ and 96.2% of housemaids’ working time,.Source: A. Implementation Details · p. 5
  • The MultiDex [8] test-set analysis reports grasp success rate, efficiency, and diversity for three classic dexterous grippers.The provided passage identifies the analysis scope but gives no metric values.Source: Table II · p. 6
  • The CVAE used a 128-dimensional latent space, β = 0.01, α = 3.0, and 100 training epochs; the PointNet++ links mapper was trained for 50 epochs.The workspace used M = 8,192 points, γ = 2.0, and confidence threshold τ = 0.8; training used batch size 128 across multiple Nvidia A100 GPUs.Source: A. Implementation Details · p. 6

B. Grasp Pose Constraints and Generation · C. Evaluation Metrics

GOAG constrains grasp generation to task-feasible object poses and samples diverse candidates across object surfaces; grasp success, generation time, and grasp diversity are evaluated with complementary metrics.

B. Grasp Pose Constraints and Generation

  • Task-specified object poses [R, T] avoid generating physically infeasible grasps, while one pose inference can cover valid configurations for objects with translational or rotational symmetry.A symmetric object such as a cylinder can be grasped with a single pose inference.Source: B. Grasp Pose Constraints and Generation · p. 6
  • For broad coverage, gripper poses are sampled uniformly on the object’s convex hull dilated by 110%, with the palm oriented opposite the hull normal.The strategy targets diverse, well-distributed grasp candidates, particularly for objects without significant symmetry.Source: B. Grasp Pose Constraints and Generation · p. 6

C. Evaluation Metrics

  • Success is tested in Isaac Gym by applying external forces along ±xyz for one second each; a grasp succeeds if the object moves less than 2 cm.The object and gripper start in optimized configuration Q∗.Source: C. Evaluation Metrics · p. 6
  • Success simulation checks practical grasp validity because force-closure estimates only theoretical stability of simplified barycenters, while joint optimization incorporates full object geometry and gripper kinematics.Contacts accepted by force-closure estimation may still be unreachable or obstructed by object shape, necessitating physical validation of the final grasp.Source: C. Evaluation Metrics · p. 6
  • Generation efficiency is the average time per grasp, calculated from producing 100 grasps with inference and optimization included but Isaac Gym simulation excluded.The timing convention follows.Source: C. Evaluation Metrics · p. 6
  • Diversity is measured as the standard deviation of gripper position (T), orientation (R), and joint values (Q), with Q variance highlighting distinct finger configurations for similar poses.Successful-grasp variability reflects object geometry and gripper kinematics filtering the reachable contact manifold, rather than arbitrary variation in initially sampled poses.Source: C. Evaluation Metrics · p. 6

D. Experimental Protocols · E. Overall Performances

GOAG achieved strong grasp performance across grippers and benchmarks despite object-agnostic training, and its vectorized optimization improves efficiency when generating many grasps.

D. Experimental Protocols

  • The in-depth comparison evaluated three grippers using 100 inference runs per method and batch size 10, with the Multidex test set covering 10 ContactDB and YCB objects.DFC was evaluated using refined CMap-Dataset grasps, and a pre-trained GenDexGrasp checkpoint was used.Source: D. Experimental Protocols · p. 6

E. Overall Performances

  • GOAG achieved the second-highest average success rate across five benchmarks, despite being trained only once on the Shadow Hand while competitors were trained per dataset.The results used a simple pose-sampling method that is less effective for larger objects; an adapted sampling strategy could improve performance there.Source: Table III · p. 7, E. Overall Performances · p. 7

D. Experimental Protocols

  • For the baseline comparison, DRO-Grasp used its configuration-invariant pre-training version with default hyperparameters and no simple grasp controller.Contact maps were generated first, then grasp poses were derived using the authors’ optimization framework.Source: D. Experimental Protocols · p. 7
  • The Shadow Hand evaluation spanned five benchmarks and 3,438 objects, including three simulated datasets, a real-world dataset, and a retargeted human-hand dataset.The protocol followed.Source: D. Experimental Protocols · p. 7

E. Overall Performances

  • On Multidex, GOAG achieved a higher average success rate across the Barrett, Allegro, and Shadow hands than the baselines while training object-agnostically.The evaluation covered 10 objects from ContactDB and YCB.Source: D. Experimental Protocols · p. 6, E. Overall Performances · p. 7
  • GOAG’s vectorized optimization is significantly faster for generating many grasps than independently optimizing each candidate, and its network has fewer parameters than DRO-Grasp’s.DRO-Grasp may be faster for a single grasp, but its processing time scales linearly with the number of grasps; GOAG also has an efficiency advantage over non-vectorized GenDexGrasp.Source: E. Overall Performances · p. 7
  • Without explicit force-closure estimation, GOAG learns robust grasp mechanics; pose sampling shapes grasp diversity, while CVAE latent sampling enables multiple grasp types for one object pose.The authors attribute the implicit learning to the expressiveness of the training set.Source: E. Overall Performances · p. 7
  • Figure 5 shows real Allegro Hand grasps on YCB objects alongside corresponding virtual grasps.The objects are rotated around the z-axis to clarify the grasp poses.Source: Figure 5 · p. 7

F. Real-Robot Experiments · V. CONCLUSION

GOAG uses gripper-centric training without object-specific grasp data and achieves competitive benchmark performance, with real-robot grasps demonstrating transfer to YCB objects.

F. Real-Robot Experiments

  • Using an Allegro Left Hand on a 7-DoF robot arm, the experiments successfully grasped 11 YCB objects.The paper presents the grasps as demonstrating transfer to real-world objects.Source: F. Real-Robot Experiments · p. 7
  • The real-robot experiments demonstrate that GOAG can transfer to real-world objects.The reported successful grasps involved 11 objects from the YCB Dataset.Source: F. Real-Robot Experiments · p. 7

V. CONCLUSION

  • GOAG eliminates the need for a large-scale, object-specific grasp database.The conclusion frames this as retaining deep learning’s strengths while removing that data requirement.Source: §V · p. 7
  • Its gripper-centric training phase makes no assumptions about object shapes and learns a generalizable grasp strategy.This is the paper’s stated basis for its grasp-planning paradigm.Source: §V · p. 7
  • GOAG performs competitively with state-of-the-art methods without training on their specific datasets, and the authors report successful real-robot deployment.The conclusion describes this as evidence of the formulation’s generalization capabilities.Source: §V · p. 8
  • The conclusion attributes GOAG’s generalization capabilities to its formulation, which is validated through a successful real-robot deployment.The conclusion also reports performance competitive with state-of-the-art methods without training on their specific datasets.Source: §V · p. 8

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