45 PAPERS • 1 BENCHMARK
Dex-Net 2.0 is a dataset associating 6.7 million point clouds and analytic grasp quality metrics with parallel-jaw grasps planned using robust quasi-static GWS analysis on a dataset of 1,500 3D object models.
29 PAPERS • NO BENCHMARKS YET
The Evolved Grasping Analysis Dataset (EGAD) comprises over 2000 generated objects aimed at training and evaluating robotic visual grasp detection algorithms. The objects in EGAD are geometrically diverse, filling a space ranging from simple to complex shapes and from easy to difficult to grasp, compared to other datasets for robotic grasping, which may be limited in size or contain only a small number of object classes.
An open database for sharing robotic experience, which provides an initial pool of 15 million video frames, from 7 different robot platforms, and study how it can be used to learn generalizable models for vision-based robotic manipulation.
21 PAPERS • NO BENCHMARKS YET
GraspNet-1Billion provides large-scale training data and a standard evaluation platform for the task of general robotic grasping. The dataset contains 97,280 RGB-D image with over one billion grasp poses.
15 PAPERS • 1 BENCHMARK
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.
2 PAPERS • NO BENCHMARKS YET
A dataset for position-constrained robot grasp planning.
Event-Stream Dataset is a robotic grasping dataset with 91 objects.
1 PAPER • NO BENCHMARKS YET
Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data. This dataset is annotated using the same protocol as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
We leverage knowledge from foundation models to introduce Grasp-Anything, a new large-scale dataset with 1M (one million) samples and 3M objects, substantially surpassing prior datasets in diversity and magnitude. In addition, Grasp-Anything can universally cover objects in our daily lives and offer a great range of object diversity.
Accompanying expert data and trained models for 2021 IROS paper on Multiview Manipulation.
Introduction NBMOD is a dataset created for researching the task of specific object grasp detection by robots in noisy environments. The dataset comprises three subsets: Simple background Single-object Subset (SSS), Noisy background Single-object Subset (NSS), and Multi-Object grasp detection Subset (MOS). The SSS subset contains 13,500 images, the NSS subset contains 13,000 images, and the MOS subset contains 5,000 images.
1 PAPER • 1 BENCHMARK
Photorealistic indoor dataset designed to enable the application of deep learning techniques to a wide variety of robotic vision problems. The RobotriX consists of hyperrealistic indoor scenes which are explored by robot agents which also interact with objects in a visually realistic manner in that simulated world.
Includes several sets of synthetic stereo images labelled with grasp rectangles representing parallel-jaw grasps (Cornell-like format).
Estimating 6D object poses is a major challenge in 3D computer vision. Building on successful instance-level approaches, research is shifting towards category-level pose estimation for practical applications. Current categorylevel datasets, however, fall short in annotation quality and pose variety. Addressing this, we introduce HouseCat6D, a new category-level 6D pose dataset. It features 1) multimodality with Polarimetric RGB and Depth (RGBD+P), 2) encompasses 194 diverse objects across 10 household categories, including two photometrically challenging ones, and 3) provides high-quality pose annotations with an error range of only 1.35 mm to 1.74 mm. The dataset also includes 4) 41 large-scale scenes with comprehensive viewpoint and occlusion coverage, 5) a checkerboard-free environment, and 6) dense 6D parallel-jaw robotic grasp annotations. Additionally, we present benchmark results for leading category-level pose estimation networks.
0 PAPER • NO BENCHMARKS YET
The KIT Whole-Body Human Motion Database is a large-scale dataset of whole-body human motion with methods and tools, which allows a unifying representation of captured human motion, and efficient search in the database, as well as the transfer of subject-specific motions to robots with different embodiments. Captured subject-specific motion is normalized regarding the subject’s height and weight by using a reference kinematics and dynamics model of the human body, the master motor map (MMM). In contrast with previous approaches and human motion databases, the motion data in this database consider not only the motions of the human subject but the position and motion of objects with which the subject is interacting as well. In addition to the description of the MMM reference model, See the paper for procedures and techniques used for the systematic recording, labeling, and organization of human motion capture data, object motions as well as the subject–object relations.