# DA2 Dataset: Toward Dexterity-Aware Dual-Arm Grasping

arXiv: https://arxiv.org/abs/2208.00408 · HTML: https://mlsanity.com/paper/2208.00408/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 31 Jul 2022.

## Builds on (most cited first, 21 of 21)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [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)
- [Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes](https://mlsanity.com/paper/2103.14127/) (2021)
- [Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours](https://mlsanity.com/paper/1509.06825/) (2015)
- [ACRONYM: A Large-Scale Grasp Dataset Based on Simulation](https://mlsanity.com/paper/2011.09584/) (2020)
- [Deep Learning for Detecting Robotic Grasps](https://mlsanity.com/paper/1301.3592/) (2013)
- [Grasp Pose Detection in Point Clouds](https://mlsanity.com/paper/1706.09911/) (2017)
- [Jacquard: A Large Scale Dataset for Robotic Grasp Detection](https://mlsanity.com/paper/1803.11469/) (2018)
- [PointNetGPD: Detecting Grasp Configurations from Point Sets](https://mlsanity.com/paper/1809.06267/) (2018)
- [Real-Time Grasp Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1412.3128/) (2014)
- [Synthesizing Diverse and Physically Stable Grasps with Arbitrary Hand Structures using Differentiable Force Closure Estimator](https://mlsanity.com/paper/2104.09194/) (2021)
- [EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation](https://mlsanity.com/paper/2003.01314/) (2020)
- [Robotic Grasp Detection using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1611.08036/) (2016)
- [Domain Randomization and Generative Models for Robotic Grasping](https://mlsanity.com/paper/1710.06425/) (2017)
- [ROI-based Robotic Grasp Detection for Object Overlapping Scenes](https://mlsanity.com/paper/1808.10313/) (2018)
- [A Billion Ways to Grasp: An Evaluation of Grasp Sampling Schemes on a Dense, Physics-based Grasp Data Set](https://mlsanity.com/paper/1912.05604/) (2019)
- [Fully Convolutional Grasp Detection Network with Oriented Anchor Box](https://mlsanity.com/paper/1803.02209/) (2018)
- [Reactive Human-to-Robot Handovers of Arbitrary Objects](https://mlsanity.com/paper/2011.08961/) (2020)
- [Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds](https://mlsanity.com/paper/2010.00824/) (2020)
- [An Integrated Simulator and Dataset that Combines Grasping and Vision for Deep Learning](https://mlsanity.com/paper/1702.02103/) (2017)

## Built on by (most cited first, 11 of 11)

- [HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios](https://mlsanity.com/paper/2212.10428/) (2022)
- [SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation](https://mlsanity.com/paper/2311.11125/) (2023)
- [Diffusion Models for Robotic Manipulation: A Survey](https://mlsanity.com/paper/2504.08438/) (2025)
- [On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks](https://mlsanity.com/paper/2303.14840/) (2023)
- [MonoGraspNet: 6-DoF Grasping with a Single RGB Image](https://mlsanity.com/paper/2209.13036/) (2022)
- [SG-Bot: Object Rearrangement via Coarse-to-Fine Robotic Imagination on Scene Graphs](https://mlsanity.com/paper/2309.12188/) (2023)
- [Constrained 6-DoF Grasp Generation on Complex Shapes for Improved Dual-Arm Manipulation](https://mlsanity.com/paper/2404.04643/) (2024)
- [Bimanual Grasp Synthesis for Dexterous Robot Hands](https://mlsanity.com/paper/2411.15903/) (2024)
- [Object Pose and Shape Estimation for Grasping: Does it Work?](https://mlsanity.com/paper/2605.26944/) (2026)
- [Grasp2Twist: Learning Bimanual Dexterous Jar Opening by Reinforcement Learning](https://mlsanity.com/paper/2609.32064/) (2026)
- [Steer2Grasp: Inference-Time Embodiment-Aware Steering for Diverse Physically Feasible Grasp Diffusion](https://mlsanity.com/paper/2609.33546/) (2026)

## Abstract (arXiv)

In this paper, we introduce DA2, the first large-scale dual-arm dexterity-aware dataset for the generation of optimal bimanual grasping pairs for arbitrary large objects. The dataset contains about 9M pairs of parallel-jaw grasps, generated from more than 6000 objects and each labeled with various grasp dexterity measures. In addition, we propose an end-to-end dual-arm grasp evaluation model trained on the rendered scenes from this dataset. We utilize the evaluation model as our baseline to show the value of this novel and nontrivial dataset by both online analysis and real robot experiments. All data and related code will be open-sourced at https://sites.google.com/view/da2dataset.