DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation (2210.02697v2)

Abstract: Robotic dexterous grasping is the first step to enable human-like dexterous object manipulation and thus a crucial robotic technology. However, dexterous grasping is much more under-explored than object grasping with parallel grippers, partially due to the lack of a large-scale dataset. In this work, we present a large-scale robotic dexterous grasp dataset, DexGraspNet, generated by our proposed highly efficient synthesis method that can be generally applied to any dexterous hand. Our method leverages a deeply accelerated differentiable force closure estimator and thus can efficiently and robustly synthesize stable and diverse grasps on a large scale. We choose ShadowHand and generate 1.32 million grasps for 5355 objects, covering more than 133 object categories and containing more than 200 diverse grasps for each object instance, with all grasps having been validated by the Isaac Gym simulator. Compared to the previous dataset from Liu et al. generated by GraspIt!, our dataset has not only more objects and grasps, but also higher diversity and quality. Via performing cross-dataset experiments, we show that training several algorithms of dexterous grasp synthesis on our dataset significantly outperforms training on the previous one. To access our data and code, including code for human and Allegro grasp synthesis, please visit our project page: https://pku-epic.github.io/DexGraspNet/.

DexGraspNet: Advancements in Dexterous Grasping through Large-Scale Simulation

The paper, "DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation," introduces DexGraspNet, a substantial dataset devoted to robotic dexterous grasping. Utilizing the ShadowHand robotic system, the dataset provides 1.32 million grasp examples across 5355 objects encompassing more than 133 categories. The significant scale and diversity of this dataset surpass previous efforts, supporting developments in dexterous manipulation, a domain less explored compared to parallel-jaw gripper-based grasping.

Research Objectives and Methods

Dexterous grasping is an essential precursor to human-like object's manipulation. This paper addresses one of the notable obstacles: the lack of large-scale datasets for dexterous hands, which hindered progress in learning-based dexterous manipulation methods. The researchers introduce a proficient synthesis approach, harnessed by a deeply accelerated differentiable force closure estimator to systematize diverse and stable grasp types systematically.

The methodology incorporates several critical innovations to enhance efficiency and robustness:

1. Enhanced Initialization Strategy: Aims to improve yield and convergence speed by systematically initializing hand poses.
2. Improved Penetration Energy Computation: Offers robustness to low-quality object meshes via reverse penetration energy.
3. Augmented Energy Terms: Introduce penalties on self-penetration and joint angle limitations to elevate grasp quality.

The authors optimized the entire data generation pipeline, resulting in the ability to process significant quantities of grasps efficiently, thereby surpassing the capabilities of GraspIt!, a commonly utilized grasp planning software in previous studies.

Results and Comparisons

Comparative analysis against the dataset from Liu et al., underscores DexGraspNet's enhanced diversity, quality, and overall scale, facilitated by improved data synthesis techniques. It allows researchers to conduct cross-dataset experiments that establish the superiority of training algorithms on DexGraspNet, reflected in better grasp success rates and diversity. The dataset's vast array of distinctly validated grasp examples reflects real-world and simulation stability, verified through the utilization of the Isaac Gym simulator.

Implications and Future Directions

DexGraspNet exemplifies a leap forward in creating expansive, varied dexterous grasp datasets, which aid in refining data-driven grasping algorithms. However, while it addresses numerous issues inherent to previous datasets, such as lack of scale and diversity, the paper acknowledges gaps in representing precision grasps and semantically guided functional grasps.

Potential future avenues of exploration include enhancing semantic understanding in grasping systems, fostering functional manipulation, and improving interactability with diverse object types utilizing dexterous hands. The long-term impact of this dataset lies in its ability to sustain and inspire innovation in complex robotic manipulation tasks.

The release of DexGraspNet sets a new standard in the field of dexterous robotic grasping. Its substantial scale and detail offer a robust foundation for advancing both theoretical knowledge and practical applications in dexterous manipulation, pointing towards evolutions in autonomous robotic systems capable of sophisticated, human-like interactions with their environment.