Comprehensive Dataset and Generative Model for Multi-Fingered Robotic Hand Grasping in Cluttered Environments

Centrala begrepp

A novel method for generating diverse and collision-free grasping datasets for multi-fingered robotic hands in cluttered environments, leveraging contact semantic and distance mapping, object affordance information, and an optimization-based grasp generation and evaluation pipeline.

Sammanfattning

The authors present a comprehensive approach for generating datasets and models for multi-fingered robotic hand grasping in cluttered environments. The key aspects of their work are:

Dataset Synthesis: They develop an optimization-based method for generating grasp samples from object meshes, considering factors like differential force closure, hand-object interaction, and robotic constraints. This allows them to create a large-scale dataset with diverse grasping poses, contact semantic and distance maps, grasp quality, collision scores, and object affordance information.

Contact Semantic Mapping: The authors introduce a Contact Semantic Conditional Variational Autoencoder (CoSe-CVAE) network that can efficiently extract contact semantic and distance maps from object point clouds. This provides rich prior information for the subsequent grasp generation process.

Grasp Generation and Evaluation: The authors propose a grasp detection method that leverages the contact representations from CoSe-CVAE to sample and refine grasp candidates. They also develop a grasp evaluation model that assesses both grasp quality and collision probability, enabling the selection of optimal grasps in cluttered scenes.

The authors demonstrate the effectiveness of their approach through extensive real-world experiments, achieving an average success rate of 81.0% in single-object grasping and 75.3% in multi-object grasping scenarios. The generated dataset and supplementary materials are made publicly available to advance research in this domain.

Statistik

The dataset includes 2,000 cluttered scenes with 1,521 household objects, along with corresponding grasp poses, grasp quality, collision scores, contact semantic and distance maps, and object affordance information.

Citat

"Our data generation method outperforms previous datasets in grasp diversity, scene diversity, modality diversity."
"Our grasp generation method has demonstrated remarkable success, outperforming established baselines with 81.0% average success rate in real-world single-object grasping and 75.3% success rate in multi-object grasping."

Viktiga insikter från

Multi-fingered Robotic Hand Grasping in Cluttered Environments through Hand-object Contact Semantic Mapping

by Lei Zhang,Ka... på arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.08844.pdf

Djupare frågor

How can the generated contact representations and affordance information be leveraged to enable more complex manipulation tasks beyond grasping, such as in-hand manipulation or tool use

The generated contact representations and affordance information can be instrumental in enabling more complex manipulation tasks beyond grasping, such as in-hand manipulation or tool use. By incorporating semantic information about object affordances, the robotic system can understand how different objects can be interacted with beyond just grasping. For in-hand manipulation, the system can utilize the contact representations to adjust the grasp based on the object's properties, allowing for more intricate manipulation tasks like rotating, flipping, or repositioning objects in the hand. Additionally, the affordance information can guide the robot in using objects as tools, understanding how they can be wielded or manipulated to achieve specific tasks. This comprehensive understanding of object properties and affordances can significantly enhance the robot's capabilities in performing a wide range of manipulation tasks with precision and efficiency.

What are the potential limitations of the proposed approach, and how could it be further improved to handle more challenging cluttered environments or a wider range of object types

While the proposed approach shows significant advancements in generating grasp candidates in cluttered environments, there are potential limitations that could be addressed for further improvement. One limitation could be the generalization capability of the model across a wider range of object types and shapes. To handle more challenging cluttered environments, the system could benefit from incorporating real-time perception and feedback mechanisms to adapt to dynamic changes in the environment. Additionally, enhancing the grasp evaluation model to consider dynamic factors like object slippage or external disturbances could improve the robustness of the system. Furthermore, exploring the integration of reinforcement learning techniques to fine-tune grasp strategies based on feedback from real-world interactions could enhance the adaptability and performance of the system in complex scenarios.

Given the advancements in generative models, how could the authors explore the use of diffusion models or other novel architectures to further enhance the diversity and realism of the generated grasping datasets

Given the advancements in generative models, such as diffusion models, there is a potential to further enhance the diversity and realism of the generated grasping datasets. By incorporating diffusion models, the system can capture complex dependencies and uncertainties in the data distribution, leading to more realistic and diverse grasp samples. Additionally, exploring the use of adversarial training techniques could help in generating more challenging and diverse grasping scenarios. Moreover, integrating self-supervised learning methods could enable the system to learn from unlabeled data and improve the generalization capability of the generated grasps across different object types and environments. By leveraging these novel architectures and techniques, the authors can push the boundaries of grasp generation and enhance the practicality and efficiency of robotic manipulation tasks.