Grasp-Anything: Large-scale Grasp Dataset from Foundation Models (2309.09818v1)

Abstract: Foundation models such as ChatGPT have made significant strides in robotic tasks due to their universal representation of real-world domains. In this paper, we leverage foundation models to tackle grasp detection, a persistent challenge in robotics with broad industrial applications. Despite numerous grasp datasets, their object diversity remains limited compared to real-world figures. Fortunately, foundation models possess an extensive repository of real-world knowledge, including objects we encounter in our daily lives. As a consequence, a promising solution to the limited representation in previous grasp datasets is to harness the universal knowledge embedded in these foundation models. We present Grasp-Anything, a new large-scale grasp dataset synthesized from foundation models to implement this solution. Grasp-Anything excels in diversity and magnitude, boasting 1M samples with text descriptions and more than 3M objects, surpassing prior datasets. Empirically, we show that Grasp-Anything successfully facilitates zero-shot grasp detection on vision-based tasks and real-world robotic experiments. Our dataset and code are available at https://grasp-anything-2023.github.io.

• The paper presents a novel approach that leverages foundation models like ChatGPT and Stable Diffusion to create a diverse dataset of over 1 million samples spanning 236 object categories.
• The dataset significantly advances zero-shot grasp detection by enabling models to generalize better in both single-object and cluttered environments.
• Real-world evaluations using a KUKA robot confirm the dataset's impact on improving grasp accuracy and enhancing robotic performance in complex scenes.

Grasp-Anything: Large-scale Grasp Dataset from Foundation Models

The paper "Grasp-Anything: Large-scale Grasp Dataset from Foundation Models" addresses the persistent challenge of grasp detection in robotics by leveraging foundation models, such as ChatGPT and Stable Diffusion, to generate a novel and extensive dataset. This paper presents an innovative approach by synthesizing a large-scale dataset known as Grasp-Anything, designed to encompass a wide variety of objects and scene arrangements typical in real-world environments. The dataset significantly surpasses previous benchmarks in both diversity and scale, providing 1 million samples with 3 million objects in total.

Grasp detection remains a critical area of research in robotics, directly impacting applications across manufacturing, logistics, and automation industries. While previous datasets have been vital for training grasp detection systems, they often suffer from limited diversity regarding objects and scene configurations, primarily due to controlled environment constraints. The emergence of foundation models offers a substantial repository of real-world knowledge, enabling the generation of diverse and realistic datasets.

Key Contributions and Methodology

1. Dataset Generation via Foundation Models: The authors employ foundation models such as ChatGPT for prompt engineering and Stable Diffusion for image generation to create a diverse corpus of objects and scene arrangements. This process comprises generating textual scene descriptions that are then translated into images, upon which grasp poses are annotated using pretrained models and analytical evaluation methods. This methodology signifies a shift towards a data-centric approach in robotic systems, aiming to provide enhanced generalization capabilities for grasp detection in unstructured environments.
2. Scale and Diversity: Grasp-Anything incorporates over 1 million samples, providing coverage of approximately 3 million individual objects. The dataset spans 236 object categories, offering a broader representation compared to existing datasets. Such scale is facilitated by the foundation models' capability to synthesize a large number of varied examples, which inherently enhances zero-shot learning potential.
3. Zero-shot Grasp Detection: An essential component of the paper is the empirical demonstration of Grasp-Anything's efficacy in zero-shot learning scenarios. Baseline grasp networks trained with Grasp-Anything show improved generalization on unseen objects compared to other datasets. Cross-dataset transfer learning experiments further validate the robustness and versatility of models trained using this dataset.
4. Real-world Robotic Evaluation: The authors validate the applicability of Grasp-Anything through real-world experiments utilizing a KUKA robot. The experiments demonstrate superior performance in both single-object and cluttered environments when models are trained on the proposed dataset, highlighting its practical feasibility in advancing robotic grasp capabilities.

Implications and Future Directions

Grasp-Anything represents a significant advancement in generating synthetic datasets that can substantially improve grasp detection models' accuracy and generalization capabilities. The introduction of a language-driven grasp dataset with diverse scene arrangements could also spawn novel research areas such as language-conditioned robotic grasping and enhanced human-robot interaction. Moreover, the paper opens pathways for further exploration into integrating 3D point cloud data, potentially bridging existing limitations in 2D grasp annotations.

The implications of utilizing foundation models for dataset generation extend beyond grasp detection, suggesting potential applications in various facets of robotics where diverse and large-scale data are requisite. The approach serves as a precursor to synthesizing datasets that reflect real-world complexity, pushing the boundaries of robotic perception and interaction.

In summary, this paper encapsulates a data-centric vision for advancing robotic grasp capabilities through the integration of foundation models, offering a robust dataset that addresses prior limitations while opening new horizons for AI and robotics research.