ACRONYM: A Large-Scale Grasp Dataset Based on Simulation

• URL: http://arxiv.org/abs/2011.09584v1
• Date: Wed, 18 Nov 2020 23:24:00 GMT
• Title: ACRONYM: A Large-Scale Grasp Dataset Based on Simulation
• Authors: Clemens Eppner, Arsalan Mousavian, Dieter Fox
• Abstract summary: ACRONYM is a dataset for robot grasp planning based on physics simulation. The dataset contains 17.7M parallel-jaw grasps, spanning 8872 objects from 262 different categories. We show the value of this large and diverse dataset by using it to train two state-of-the-art learning-based grasp planning algorithms.
• Score: 64.37675024289857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce ACRONYM, a dataset for robot grasp planning based on physics simulation. The dataset contains 17.7M parallel-jaw grasps, spanning 8872 objects from 262 different categories, each labeled with the grasp result obtained from a physics simulator. We show the value of this large and diverse dataset by using it to train two state-of-the-art learning-based grasp planning algorithms. Grasp performance improves significantly when compared to the original smaller dataset. Data and tools can be accessed at https://sites.google.com/nvidia.com/graspdataset.

Related papers

• UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction [93.77809355002591]
  We introduce UniTraj, a comprehensive framework that unifies various datasets, models, and evaluation criteria. We conduct extensive experiments and find that model performance significantly drops when transferred to other datasets. We provide insights into dataset characteristics to explain these findings.
  arXiv  Detail & Related papers  (2024-03-22T10:36:50Z)
• Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation [2.240286607818126]
  We propose a protocol called flows for flows for training normalizing flows to morph one dataset into another. This enables a morphing strategy trained with maximum likelihood estimation. We show how to condition the learned flows on particular features in order to create a morphing function for every value of the conditioning feature.
  arXiv  Detail & Related papers  (2023-09-12T18:00:01Z)
• Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers [23.589419066824306]
  Estimating fluid dynamics is a notoriously hard problem to solve. We introduce a new model, method and benchmark for the problem. We show that our transformer outperforms state-of-the-art performance on, both, existing synthetic and real datasets.
  arXiv  Detail & Related papers  (2023-02-16T12:59:08Z)
• Large Scale Real-World Multi-Person Tracking [68.27438015329807]
  This paper presents a new large scale multi-person tracking dataset -- textttPersonPath22. It is over an order of magnitude larger than currently available high quality multi-object tracking datasets such as MOT17, HiEve, and MOT20.
  arXiv  Detail & Related papers  (2022-11-03T23:03:13Z)
• DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation [10.783992625475081]
  We present a large-scale simulated dataset, DexGraspNet, for robotic dexterous grasping. We use ShadowHand, a dexterous gripper commonly seen in robotics, to generate 1.32 million grasps for 5355 objects. Compared to the previous dataset generated by GraspIt!, our dataset has not only more objects and grasps, but also higher diversity and quality.
  arXiv  Detail & Related papers  (2022-10-06T06:09:16Z)
• DA$^2$ Dataset: Toward Dexterity-Aware Dual-Arm Grasping [58.48762955493929]
  DA$2$ is 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.
  arXiv  Detail & Related papers  (2022-07-31T10:02:27Z)
• PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data [7.214140640112874]
  This paper addresses the task of extracting meaningful results in an automated manner from high dimensional data sets. We propose deep learning methods which are capable of processing such data and which can be trained to solve relevant tasks on simulation data. We compile a large dataset of 2D simulations of the flow field around airfoils which contains 16000 flow fields with which we tested and compared approaches.
  arXiv  Detail & Related papers  (2021-03-11T09:28:00Z)
• AutoSimulate: (Quickly) Learning Synthetic Data Generation [70.82315853981838]
  We propose an efficient alternative for optimal synthetic data generation based on a novel differentiable approximation of the objective. We demonstrate that the proposed method finds the optimal data distribution faster (up to $50times$), with significantly reduced training data generation (up to $30times$) and better accuracy ($+8.7%$) on real-world test datasets than previous methods.
  arXiv  Detail & Related papers  (2020-08-16T11:36:11Z)
• Measures of Complexity for Large Scale Image Datasets [0.3655021726150368]
  In this work, we build a series of relatively simple methods to measure the complexity of a dataset. We present our analysis using four datasets from the autonomous driving research community - Cityscapes, IDD, BDD and Vistas. Using entropy based metrics, we present a rank-order complexity of these datasets, which we compare with an established rank-order with respect to deep learning.
  arXiv  Detail & Related papers  (2020-08-10T21:54:23Z)
• Open Graph Benchmark: Datasets for Machine Learning on Graphs [86.96887552203479]
  We present the Open Graph Benchmark (OGB) to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics.
  arXiv  Detail & Related papers  (2020-05-02T03:09:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.