# A Billion Ways to Grasp: An Evaluation of Grasp Sampling Schemes on a Dense, Physics-based Grasp Data Set

arXiv: https://arxiv.org/abs/1912.05604 · HTML: https://mlsanity.com/paper/1912.05604/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

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

- [DexYCB: A Benchmark for Capturing Hand Grasping of Objects](https://mlsanity.com/paper/2104.04631/) (2021)
- [Graspness Discovery in Clutters for Fast and Accurate Grasp Detection](https://mlsanity.com/paper/2406.11142/) (2024)
- [Learning Human-to-Robot Handovers from Point Clouds](https://mlsanity.com/paper/2303.17592/) (2023)
- [Grasp-Anything: Large-scale Grasp Dataset from Foundation Models](https://mlsanity.com/paper/2309.09818/) (2023)
- [Language-driven Grasp Detection](https://mlsanity.com/paper/2406.09489/) (2024)
- [Predicting Stable Configurations for Semantic Placement of Novel Objects](https://mlsanity.com/paper/2108.12062/) (2021)
- [MonoGraspNet: 6-DoF Grasping with a Single RGB Image](https://mlsanity.com/paper/2209.13036/) (2022)
- [HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers](https://mlsanity.com/paper/2205.09747/) (2022)
- [Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimation](https://mlsanity.com/paper/2108.02425/) (2021)
- [MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis](https://mlsanity.com/paper/2208.03963/) (2022)
- [DA2 Dataset: Toward Dexterity-Aware Dual-Arm Grasping](https://mlsanity.com/paper/2208.00408/) (2022)
- [Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input](https://mlsanity.com/paper/2209.08752/) (2022)
- [Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic Benchmark](https://mlsanity.com/paper/2305.16378/) (2023)
- [Learning suction graspability considering grasp quality and robot reachability for bin-picking](https://mlsanity.com/paper/2111.02571/) (2021)
- [Video based Object 6D Pose Estimation using Transformers](https://mlsanity.com/paper/2210.13540/) (2022)
- [KGNv2: Separating Scale and Pose Prediction for Keypoint-based 6-DoF Grasp Synthesis on RGB-D input](https://mlsanity.com/paper/2303.05617/) (2023)
- [Speeding up 6-DoF Grasp Sampling with Quality-Diversity](https://mlsanity.com/paper/2403.06173/) (2024)
- [Grasping Trajectory Optimization with Point Clouds](https://mlsanity.com/paper/2403.05466/) (2024)
- [QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversity](https://mlsanity.com/paper/2410.02319/) (2024)
- [VideoPose: Estimating 6D object pose from videos](https://mlsanity.com/paper/2111.10677/) (2021)
- [6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic Grasping](https://mlsanity.com/paper/2208.14288/) (2022)
- [COMBO-Grasp: Learning Constraint-Based Manipulation for Bimanual Occluded Grasping](https://mlsanity.com/paper/2502.08054/) (2025)
- [QuickGrasp: Lightweight Antipodal Grasp Planning with Point Clouds](https://mlsanity.com/paper/2504.19716/) (2025)
- [RealVLG-R1: A Large-Scale Real-World Visual-Language Grounding Benchmark for Robotic Perception and Manipulation](https://mlsanity.com/paper/2603.14880/) (2026)
- [Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping](https://mlsanity.com/paper/2304.08805/) (2023)
- [Sim-MEES: Modular End-Effector System Grasping Dataset for Mobile Manipulators in Cluttered Environments](https://mlsanity.com/paper/2305.10580/) (2023)
- [Sim-Grasp: Learning 6-DOF Grasp Policies for Cluttered Environments Using a Synthetic Benchmark](https://mlsanity.com/paper/2405.00841/) (2024)
- [GraspFactory: A Large Object-Centric Grasping Dataset](https://mlsanity.com/paper/2509.20550/) (2025)
- [Placeit! A Framework for Learning Robot Object Placement Skills](https://mlsanity.com/paper/2510.09267/) (2025)
- [Object Pose and Shape Estimation for Grasping: Does it Work?](https://mlsanity.com/paper/2605.26944/) (2026)

## Abstract (arXiv)

Robot grasping is often formulated as a learning problem. With the increasing speed and quality of physics simulations, generating large-scale grasping data sets that feed learning algorithms is becoming more and more popular. An often overlooked question is how to generate the grasps that make up these data sets. In this paper, we review, classify, and compare different grasp sampling strategies. Our evaluation is based on a fine-grained discretization of SE(3) and uses physics-based simulation to evaluate the quality and robustness of the corresponding parallel-jaw grasps. Specifically, we consider more than 1 billion grasps for each of the 21 objects from the YCB data set. This dense data set lets us evaluate existing sampling schemes w.r.t. their bias and efficiency. Our experiments show that some popular sampling schemes contain significant bias and do not cover all possible ways an object can be grasped.