# UGG: Unified Generative Grasping

arXiv: https://arxiv.org/abs/2311.16917 · HTML: https://mlsanity.com/paper/2311.16917/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 61 of its 88 references that are in the map.
- First posted on arXiv 28 Nov 2023.

## Builds on (most cited first, 30 of 61)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Diffusion Probabilistic Models for 3D Point Cloud Generation](https://mlsanity.com/paper/2103.01458/) (2021)
- [LION: Latent Point Diffusion Models for 3D Shape Generation](https://mlsanity.com/paper/2210.06978/) (2022)
- [3D Shape Generation and Completion through Point-Voxel Diffusion](https://mlsanity.com/paper/2104.03670/) (2021)
- [GRAB: A Dataset of Whole-Body Human Grasping of Objects](https://mlsanity.com/paper/2008.11200/) (2020)
- [Point-Voxel CNN for Efficient 3D Deep Learning](https://mlsanity.com/paper/1907.03739/) (2019)
- [SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation](https://mlsanity.com/paper/2212.04493/) (2022)
- [3D Neural Field Generation using Triplane Diffusion](https://mlsanity.com/paper/2211.16677/) (2022)
- [Diffusion-based Generation, Optimization, and Planning in 3D Scenes](https://mlsanity.com/paper/2301.06015/) (2023)
- [DiffRF: Rendering-Guided 3D Radiance Field Diffusion](https://mlsanity.com/paper/2212.01206/) (2022)
- [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://mlsanity.com/paper/2211.08332/) (2022)
- [MeshDiffusion: Score-based Generative 3D Mesh Modeling](https://mlsanity.com/paper/2303.08133/) (2023)
- [Shape As Points: A Differentiable Poisson Solver](https://mlsanity.com/paper/2106.03452/) (2021)
- [One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale](https://mlsanity.com/paper/2303.06555/) (2023)
- [Geometric Latent Diffusion Models for 3D Molecule Generation](https://mlsanity.com/paper/2305.01140/) (2023)

## Built on by (most cited first, 30 of 30)

- [DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping](https://mlsanity.com/paper/2502.20900/) (2025)
- [DexDiffuser: Generating Dexterous Grasps with Diffusion Models](https://mlsanity.com/paper/2402.02989/) (2024)
- [Diffusion Models for Robotic Manipulation: A Survey](https://mlsanity.com/paper/2504.08438/) (2025)
- [HUMOTO: A 4D Dataset of Mocap Human Object Interactions](https://mlsanity.com/paper/2504.10414/) (2025)
- [SemGrasp: Semantic Grasp Generation via Language Aligned Discretization](https://mlsanity.com/paper/2404.03590/) (2024)
- [DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness](https://mlsanity.com/paper/2503.08257/) (2025)
- [Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation](https://mlsanity.com/paper/2506.17198/) (2025)
- [InterDyn: Controllable Interactive Dynamics with Video Diffusion Models](https://mlsanity.com/paper/2412.11785/) (2024)
- [GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping](https://mlsanity.com/paper/2503.04123/) (2025)
- [Text2Grasp: Grasp synthesis by text prompts of object grasping parts](https://mlsanity.com/paper/2404.15189/) (2024)
- [EvolvingGrasp: Evolutionary Grasp Generation via Efficient Preference Alignment](https://mlsanity.com/paper/2503.14329/) (2025)
- [Dexterous Functional Pre-Grasp Manipulation with Diffusion Policy](https://mlsanity.com/paper/2403.12421/) (2024)
- [Joint Diffusion for Universal Hand-Object Grasp Generation](https://mlsanity.com/paper/2409.04560/) (2024)
- [DextER: Language-driven Dexterous Grasp Generation with Embodied Reasoning](https://mlsanity.com/paper/2601.16046/) (2026)
- [Bimanual Grasp Synthesis for Dexterous Robot Hands](https://mlsanity.com/paper/2411.15903/) (2024)
- [Grasping a Handful: Sequential Multi-Object Dexterous Grasp Generation](https://mlsanity.com/paper/2503.22370/) (2025)
- [Contact Map Transfer with Conditional Diffusion Model for Generalizable Dexterous Grasp Generation](https://mlsanity.com/paper/2511.01276/) (2025)
- [Grasp2Grasp: Vision-Based Dexterous Grasp Translation via Schr"odinger Bridges](https://mlsanity.com/paper/2506.02489/) (2025)
- [UniMorphGrasp: Diffusion Model with Morphology-Awareness for Cross-Embodiment Dexterous Grasp Generation](https://mlsanity.com/paper/2602.00915/) (2026)
- [EFF-Grasp: Energy-Field Flow Matching for Physics-Aware Dexterous Grasp Generation](https://mlsanity.com/paper/2603.16151/) (2026)
- [HO-Flow: Generalizable Hand-Object Interaction Generation with Latent Flow Matching](https://mlsanity.com/paper/2604.10836/) (2026)
- [GenHeld: Generating and Editing Handheld Objects](https://mlsanity.com/paper/2406.05059/) (2024)
- [Leveraging CVAE for Joint Configuration Estimation of Multifingered Grippers from Point Cloud Data](https://mlsanity.com/paper/2511.17276/) (2025)
- [SECOND-Grasp: Semantic Contact-guided Dexterous Grasping](https://mlsanity.com/paper/2605.13117/) (2026)
- [Geometry-Guided Modeling of Foundation Features Enables Generalizable Object Shape Deformation Learning](https://mlsanity.com/paper/2605.29661/) (2026)
- [NDPP-Grasp: Non-Differentiable Physical Plausibility Constraint-Guided Task-Oriented Dexterous Grasp Generation](https://mlsanity.com/paper/2606.02432/) (2026)
- [KPGrasp: Scalable Keypoint Flow Matching for Dexterous Grasp Generation](https://mlsanity.com/paper/2606.09314/) (2026)
- [WristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation](https://mlsanity.com/paper/2607.06438/) (2026)
- [GOAG: Generative and Object-Agnostic Grasp Planner for Dexterous Robotic Manipulation](https://mlsanity.com/paper/2608.19759/) (2026)
- [CoToGrasp: Contact-Topology-Conditioned Dexterous Grasp Synthesis via Canonical Workspace Learning](https://mlsanity.com/paper/2608.19776/) (2026)

## Abstract (arXiv)

Dexterous grasping aims to produce diverse grasping postures with a high grasping success rate. Regression-based methods that directly predict grasping parameters given the object may achieve a high success rate but often lack diversity. Generation-based methods that generate grasping postures conditioned on the object can often produce diverse grasping, but they are insufficient for high grasping success due to lack of discriminative information. To mitigate, we introduce a unified diffusion-based dexterous grasp generation model, dubbed the name UGG, which operates within the object point cloud and hand parameter spaces. Our all-transformer architecture unifies the information from the object, the hand, and the contacts, introducing a novel representation of contact points for improved contact modeling. The flexibility and quality of our model enable the integration of a lightweight discriminator, benefiting from simulated discriminative data, which pushes for a high success rate while preserving high diversity. Beyond grasp generation, our model can also generate objects based on hand information, offering valuable insights into object design and studying how the generative model perceives objects. Our model achieves state-of-the-art dexterous grasping on the large-scale DexGraspNet dataset while facilitating human-centric object design, marking a significant advancement in dexterous grasping research. Our project page is https://jiaxin-lu.github.io/ugg/.