# DDGC: Generative Deep Dexterous Grasping in Clutter

arXiv: https://arxiv.org/abs/2103.04783 · HTML: https://mlsanity.com/paper/2103.04783/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://mlsanity.com/paper/1603.02199/) (2016)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [6-DOF GraspNet: Variational Grasp Generation for Object Manipulation](https://mlsanity.com/paper/1905.10520/) (2019)
- [Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach](https://mlsanity.com/paper/1804.05172/) (2018)
- [Grasp Pose Detection in Point Clouds](https://mlsanity.com/paper/1706.09911/) (2017)
- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [Shape Completion Enabled Robotic Grasping](https://mlsanity.com/paper/1609.08546/) (2016)
- [6-DOF Grasping for Target-driven Object Manipulation in Clutter](https://mlsanity.com/paper/1912.03628/) (2019)
- [EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation](https://mlsanity.com/paper/2003.01314/) (2020)
- [High precision grasp pose detection in dense clutter](https://mlsanity.com/paper/1603.01564/) (2016)
- [Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps](https://mlsanity.com/paper/2012.09696/) (2020)
- [Robust Grasp Planning Over Uncertain Shape Completions](https://mlsanity.com/paper/1903.00645/) (2019)
- [Multi-Modal Geometric Learning for Grasping and Manipulation](https://mlsanity.com/paper/1803.07671/) (2018)
- [Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity](https://mlsanity.com/paper/2009.13146/) (2020)
- [Multi-Fingered Active Grasp Learning](https://mlsanity.com/paper/2006.05264/) (2020)
- [Beyond Top-Grasps Through Scene Completion](https://mlsanity.com/paper/1909.12908/) (2019)
- [Deep Dexterous Grasping of Novel Objects from a Single View](https://mlsanity.com/paper/1908.04293/) (2019)

## Built on by (most cited first, 29 of 29)

- [DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation](https://mlsanity.com/paper/2210.02697/) (2022)
- [DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping](https://mlsanity.com/paper/2502.20900/) (2025)
- [GenDexGrasp: Generalizable Dexterous Grasping](https://mlsanity.com/paper/2210.00722/) (2022)
- [DexDiffuser: Generating Dexterous Grasps with Diffusion Models](https://mlsanity.com/paper/2402.02989/) (2024)
- [DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenes](https://mlsanity.com/paper/2410.23004/) (2024)
- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [RealDex: Towards Human-like Grasping for Robotic Dexterous Hand](https://mlsanity.com/paper/2402.13853/) (2024)
- [UGG: Unified Generative Grasping](https://mlsanity.com/paper/2311.16917/) (2023)
- [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)
- [Grasp Multiple Objects with One Hand](https://mlsanity.com/paper/2310.15599/) (2023)
- [AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency](https://mlsanity.com/paper/2502.16420/) (2025)
- [ContactDexNet: Multi-fingered Robotic Hand Grasping in Cluttered Environments through Hand-object Contact Semantic Mapping](https://mlsanity.com/paper/2404.08844/) (2024)
- [Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection](https://mlsanity.com/paper/2203.05187/) (2022)
- [GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping](https://mlsanity.com/paper/2503.04123/) (2025)
- [EvolvingGrasp: Evolutionary Grasp Generation via Efficient Preference Alignment](https://mlsanity.com/paper/2503.14329/) (2025)
- [Simulation-based Bayesian inference for multi-fingered robotic grasping](https://mlsanity.com/paper/2109.14275/) (2021)
- [Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering](https://mlsanity.com/paper/2306.07392/) (2023)
- [Single-View Scene Point Cloud Human Grasp Generation](https://mlsanity.com/paper/2404.15815/) (2024)
- [V-MAO: Generative Modeling for Multi-Arm Manipulation of Articulated Objects](https://mlsanity.com/paper/2111.03987/) (2021)
- [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)
- [Planning Visual-Tactile Precision Grasps via Complementary Use of Vision and Touch](https://mlsanity.com/paper/2212.08604/) (2022)
- [Grasp2Grasp: Vision-Based Dexterous Grasp Translation via Schr"odinger Bridges](https://mlsanity.com/paper/2506.02489/) (2025)
- [CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Network](https://mlsanity.com/paper/2303.13182/) (2023)
- [DMFC-GraspNet: Differentiable Multi-Fingered Robotic Grasp Generation in Cluttered Scenes](https://mlsanity.com/paper/2308.00456/) (2023)
- [AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection](https://mlsanity.com/paper/2606.23689/) (2026)
- [D3Grasp: Diverse and Deformable Dexterous Grasping for General Objects](https://mlsanity.com/paper/2509.19892/) (2025)
- [BLaDA: Bridging Language to Functional Dexterous Actions within 3DGS Fields](https://mlsanity.com/paper/2604.08410/) (2026)

## Abstract (arXiv)

Recent advances in multi-fingered robotic grasping have enabled fast 6-Degrees-Of-Freedom (DOF) single object grasping. Multi-finger grasping in cluttered scenes, on the other hand, remains mostly unexplored due to the added difficulty of reasoning over obstacles which greatly increases the computational time to generate high-quality collision-free grasps. In this work we address such limitations by introducing DDGC, a fast generative multi-finger grasp sampling method that can generate high quality grasps in cluttered scenes from a single RGB-D image. DDGC is built as a network that encodes scene information to produce coarse-to-fine collision-free grasp poses and configurations. We experimentally benchmark DDGC against the simulated-annealing planner in GraspIt! on 1200 simulated cluttered scenes and 7 real world scenes. The results show that DDGC outperforms the baseline on synthesizing high-quality grasps and removing clutter while being 5 times faster. This, in turn, opens up the door for using multi-finger grasps in practical applications which has so far been limited due to the excessive computation time needed by other methods.