Dexonomy: Synthesizing All Dexterous Grasp Types in a Grasp Taxonomy

• URL: http://arxiv.org/abs/2504.18829v1
• Date: Sat, 26 Apr 2025 07:32:59 GMT
• Title: Dexonomy: Synthesizing All Dexterous Grasp Types in a Grasp Taxonomy
• Abstract summary: Generalizable dexterous grasping with suitable grasp types is a fundamental skill for intelligent robots.<n>Existing automatic grasp synthesis methods are often limited to specific grasp types or object categories.<n>This work proposes an efficient pipeline capable of synthesizing contact-rich, penetration-free, and physically plausible grasps for any grasp type, object, and articulated hand.
• Score: 9.52211166462638
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generalizable dexterous grasping with suitable grasp types is a fundamental skill for intelligent robots. Developing such skills requires a large-scale and high-quality dataset that covers numerous grasp types (i.e., at least those categorized by the GRASP taxonomy), but collecting such data is extremely challenging. Existing automatic grasp synthesis methods are often limited to specific grasp types or object categories, hindering scalability. This work proposes an efficient pipeline capable of synthesizing contact-rich, penetration-free, and physically plausible grasps for any grasp type, object, and articulated hand. Starting from a single human-annotated template for each hand and grasp type, our pipeline tackles the complicated synthesis problem with two stages: optimize the object to fit the hand template first, and then locally refine the hand to fit the object in simulation. To validate the synthesized grasps, we introduce a contact-aware control strategy that allows the hand to apply the appropriate force at each contact point to the object. Those validated grasps can also be used as new grasp templates to facilitate future synthesis. Experiments show that our method significantly outperforms previous type-unaware grasp synthesis baselines in simulation. Using our algorithm, we construct a dataset containing 10.7k objects and 9.5M grasps, covering 31 grasp types in the GRASP taxonomy. Finally, we train a type-conditional generative model that successfully performs the desired grasp type from single-view object point clouds, achieving an 82.3% success rate in real-world experiments. Project page: https://pku-epic.github.io/Dexonomy.

Related papers

• CoToGrasp: Contact-Topology-Conditioned Dexterous Grasp Synthesis via Canonical Workspace Learning [3.906714757786654]
  CoToGrasp is a novel generative framework that synthesizes diverse, stable grasps strictly conditioned on specific contact topologies.<n>CoToGrasp achieves state-of-the-art performance, outperforming existing taxonomy-guided planners.
  arXiv  Detail & Related papers  (2026-08-20T08:19:44Z)
• HUGS: Guiding Unified Dexterous Grasp Synthesis Across Modes and Scales via Learned Human Priors [16.091967944555382]
  We present HUGS (Human-prior-guided Unified Dexterous Grasp Synthesis), a human-prior-guided grasp synthesis across modes and scales.<n>Instead of balance directly human demonstrations, HUGS learns an object-guided human prior that captures human grasp preferences.<n>With HUGS, we synthesize 3.2M robotic grasps over 157K scenes, spanning object half-diagonal lengths from 2 cm to 30 cm and modes from two-finger to bimanual grasps.
  arXiv  Detail & Related papers  (2026-07-06T00:01:19Z)
• BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes [64.2030401965582]
  BiDexGrasp consists of a large-scale bimanual dexterous grasp dataset and a novel generation model.<n>We construct a large-scale bimanual dexterous grasp dataset, comprising 6351 diverse objects with sizes ranging from 30 to 80 cm.<n>We introduce a bimanual-adaptive and geometry-size-adaptive dexterous grasping generation framework.
  arXiv  Detail & Related papers  (2026-04-08T02:17:11Z)
• UltraDexGrasp: Learning Universal Dexterous Grasping for Bimanual Robots with Synthetic Data [70.96218513410454]
  We introduce UltraDexGrasp, a framework for universal dexterous grasping with bimanual robots.<n>The proposed data-generation pipeline integrates optimization-based grasp synthesis with planning-based demonstration generation.<n>Based on UltraDexGrasp-20M, we develop a simple yet effective grasp policy that takes point clouds as input, aggregates scene features via unidirectional attention, and predicts control commands.
  arXiv  Detail & Related papers  (2026-03-05T15:54:21Z)
• VCoT-Grasp: Grasp Foundation Models with Visual Chain-of-Thought Reasoning for Language-driven Grasp Generation [67.98487725287835]
  VCoT-Grasp is an end-to-end grasp foundation model that incorporates visual chain-of-thought reasoning to enhance visual understanding for grasp generation.<n>For training, we refine and introduce a large-scale dataset, VCoT-GraspSet, comprising 167K synthetic images with over 1.36M grasps.<n>Our method significantly improves grasp success rates and generalizes effectively to unseen objects, backgrounds, and distractors.
  arXiv  Detail & Related papers  (2025-10-07T11:50:26Z)
• CEDex: Cross-Embodiment Dexterous Grasp Generation at Scale from Human-like Contact Representations [53.37721117405022]
  Cross-embodiment dexterous grasp synthesis refers to adaptively generating and optimizing grasps for various robotic hands.<n>We propose CEDex, a novel cross-embodiment dexterous grasp synthesis method at scale.<n>We construct the largest cross-embodiment grasp dataset to date, comprising 500K objects across four types with 20M total grasps.
  arXiv  Detail & Related papers  (2025-09-29T12:08:04Z)
• SIGHT: Single-Image Conditioned Generation of Hand Trajectories for Hand-Object Interaction [86.54738165527502]
  We introduce a novel task of generating realistic and diverse 3D hand trajectories given a single image of an object.<n>Hand-object interaction trajectory priors can greatly benefit applications in robotics, embodied AI, augmented reality and related fields.
  arXiv  Detail & Related papers  (2025-03-28T20:53:20Z)
• Scaling Laws of Synthetic Data for Language Models [132.67350443447611]
  We introduce SynthLLM, a scalable framework that transforms pre-training corpora into diverse, high-quality synthetic datasets.<n>Our approach achieves this by automatically extracting and recombining high-level concepts across multiple documents using a graph algorithm.
  arXiv  Detail & Related papers  (2025-03-25T11:07:12Z)
• Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated Objects via Procedural Generation [22.500531114325092]
  We propose Infinite Mobility, a novel method for high-fidelity articulated objects through procedural generation.<n>We show that our synthetic data can be used as training data for generative models, enabling next-step scaling up.
  arXiv  Detail & Related papers  (2025-03-17T17:53:56Z)
• SynFER: Towards Boosting Facial Expression Recognition with Synthetic Data [78.70620682374624]
  We introduce SynFER, a novel framework for synthesizing facial expression image data based on high-level textual descriptions.<n>To ensure the quality and reliability of the synthetic data, we propose a semantic guidance technique and a pseudo-label generator.<n>Results validate the efficacy of our approach and the synthetic data.
  arXiv  Detail & Related papers  (2024-10-13T14:58:21Z)
• Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines [74.42485647685272]
  We focus on Generative Masked Language Models (GMLMs) We train a model to fit conditional probabilities of the data distribution via masking, which are subsequently used as inputs to a Markov Chain to draw samples from the model. We adapt the T5 model for iteratively-refined parallel decoding, achieving 2-3x speedup in machine translation with minimal sacrifice in quality.
  arXiv  Detail & Related papers  (2024-07-22T18:00:00Z)
• SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation [55.2480439325792]
  We study the synthesis of six datasets, covering topic classification, sentiment analysis, tone detection, and humor. We find that SynthesizRR greatly improves lexical and semantic diversity, similarity to human-written text, and distillation performance.
  arXiv  Detail & Related papers  (2024-05-16T12:22:41Z)
• Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models [69.76066070227452]
  *Data Synthesis* is a promising way to train a small model with very little labeled data. We propose *Synthesis Step by Step* (**S3**), a data synthesis framework that shrinks this distribution gap. Our approach improves the performance of a small model by reducing the gap between the synthetic dataset and the real data.
  arXiv  Detail & Related papers  (2023-10-20T17:14:25Z)
• DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation [10.783992625475081]
  We present a large-scale simulated dataset, DexGraspNet, for robotic dexterous grasping. We use ShadowHand, a dexterous gripper commonly seen in robotics, to generate 1.32 million grasps for 5355 objects. Compared to the previous dataset generated by GraspIt!, our dataset has not only more objects and grasps, but also higher diversity and quality.
  arXiv  Detail & Related papers  (2022-10-06T06:09:16Z)
• Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis [6.517935794312337]
  We present an eye-in-hand learning-based approach for hand pre-shape classification from RGB sequences. We tackle the peculiarity of the eye-in-hand setting by means of a model for the human arm trajectories.
  arXiv  Detail & Related papers  (2022-03-18T09:16:48Z)
• Universalizing Weak Supervision [18.832796698152492]
  We propose a universal technique that enables weak supervision over any label type. We apply this technique to important problems previously not tackled by WS frameworks including learning to rank, regression, and learning in hyperbolic space.
  arXiv  Detail & Related papers  (2021-12-07T17:59:10Z)
• A Deep Learning Generative Model Approach for Image Synthesis of Plant Leaves [62.997667081978825]
  We generate via advanced Deep Learning (DL) techniques artificial leaf images in an automatized way. We aim to dispose of a source of training samples for AI applications for modern crop management.
  arXiv  Detail & Related papers  (2021-11-05T10:53:35Z)
• Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets [90.61266099147053]
  We investigate efficient annotation strategies for collecting multi-class classification labels for a large collection of images. We propose modifications and best practices aimed at minimizing human labeling effort. Simulated experiments on a 125k image subset of the ImageNet100 show that it can be annotated to 80% top-1 accuracy with 0.35 annotations per image on average.
  arXiv  Detail & Related papers  (2021-04-26T16:29:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.