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

An Dinh Vuong, Minh Nhat Vu, Hieu Le, Baoru Huang, Binh Huynh, Thieu Vo, Andreas Kugi, Anh Nguyen

arXiv:2309.09818v1cs.ROcs.CV

TL;DR

Existing grasp datasets have limited object diversity and scene coverage, motivating a more universal grasp-detection dataset. Grasp-Anything uses foundation-model knowledge to synthesize large-scale multimodal grasp data, and the authors report zero-shot utility in vision and real-world robotics. The dataset contains 1M samples and 3M objects and is intended to support future language-driven grasping research.

Abstract

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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.

I. INTRODUCTION

Grasp detection remains difficult to generalize because existing datasets contain limited objects, omit natural-language scene descriptions, and rely on controlled arrangements. Grasp-Anything addresses this gap by using foundation-model knowledge to create a large, diverse dataset and evaluates its zero-shot utility in vision and robotics.

  • Grasp detection supports manufacturing, logistics, and warehouse automation, but model-centric methods can be unstable across datasets and real-world data.Source: §I · p. 1
  • Existing grasp datasets limit object diversity, omit natural-language descriptions, and often use bin-like or laboratory-controlled scenes unlike natural settings.Source: §I · p. 1
  • Foundation models provide broad real-world knowledge that can help generate diverse objects and scene arrangements for grasp-dataset synthesis.Source: §I · p. 1
  • The dataset supports zero-shot grasp detection in computer vision and real-world robotic experiments, with reported improvements over related datasets.Source: §I · p. 2
  • Grasp-Anything contains 1M samples and 3M objects, substantially surpassing prior datasets in diversity and magnitude.Source: §I · p. 2

II. RELATED WORK

Related work shows that grasp detection has advanced through deep learning, yet real-world deployment remains constrained by limited dataset size and diversity. The paper therefore positions Grasp-Anything as a large-scale benchmark built with foundation models for universal-object grasping.

  • Existing grasp datasets restrict object diversity and scene configurations, making real-world grasping applications challenging.Source: §II · p. 2
  • Deep-learning grasp detectors are widely used, but applying them to real-world grasping remains difficult because available grasp data are limited in size and diversity.Source: §II · p. 2
  • Prior zero-shot grasp work focuses mainly on sim2real applications rather than benchmarking the zero-shot feasibility of existing grasp datasets.Source: §II · p. 2
  • Foundation-model approaches in robotics have scope, functionality, and interaction limitations, motivating this paper’s data-centric use of LLM and T2I models.Source: §II · p. 2

III. THE GRASP-ANYTHING DATASET

Grasp-Anything uses language and image foundation models to synthesize scenes, images, object masks, and grasp annotations at scale. Its pipeline combines prompt generation, image synthesis, grounding, segmentation, and 2D grasp representation.

III. THE GRASP-ANYTHING DATASET

  • The dataset pipeline generates scene descriptions, synthesizes aligned images, obtains object masks, and automatically generates and evaluates 2D grasp poses.Source: §III · p. 2
  • The method represents grasp poses as 2D rectangles because this format is simple and compatible with real-world parallel-plate grippers.Source: §III · p. 2

A. Scene Generation

  • ChatGPT prompt engineering is used to produce diverse scene descriptions aimed at covering a universal set of graspable objects.Source: A. Scene Generation · p. 3
  • The prompt template contains scene-arrangement text and an extracted list of graspable objects, providing structured inputs for later annotation.Source: A. Scene Generation · p. 3
  • Context augmentation samples prior descriptions from a prompt buffer and feeds them back to ChatGPT to maintain generation quality over long-term synthesis.Source: A. Scene Generation · p. 3
  • Stable Diffusion 2.1 generates images from the descriptions, while OFA and Segment Anything produce grounding masks for referenced objects.Source: A. Scene Generation · p. 3

B. Grasp Pose Annotation

Grasp poses are generated by a pretrained model and evaluated using a torque-based quality measure that avoids requiring unknown physical parameters. The procedure ranks grasps by a resistance-to-torque ratio and labels positive grasps accordingly.

B. Grasp Pose Annotation

  • Candidate grasp poses are generated with pretrained RAGT-3/3 and further evaluated using Kamon et al.’s traditional method.Source: B. Grasp Pose Annotation · p. 3
  • The net torque formulation uses object, friction, contact-geometry, and force parameters assumed constant across grasps, leaving R, α1, and α2 as pose variables.Source: B. Grasp Pose Annotation · p. 3
  • The resistance-to-torque ratio ˜T ranks grasp quality, with positive values labeled positive grasps and nonpositive values labeled negative.Source: B. Grasp Pose Annotation · p. 4

C. Grasp-Anything Statistics

Grasp-Anything exhibits broad category, language, object-shape, and object-count diversity. Its statistics substantially exceed the comparison datasets in category coverage, object quantity, and spatial shape spread.

Number of Categories

Number of Objects

Number of POS tags

Object Shape Distribution

D. How will Grasp-Anything be helpful to the community?

Grasp-Anything is intended to support future research through its large-scale multimodal data, broad object coverage, natural scenes, and diverse language descriptions. The proposed applications include zero-shot and language-driven grasping alongside related robotic tasks.

D. How will Grasp-Anything be helpful to the community?

Grasp Detection

Language-driven Grasping

Related Applications

IV. EXPERIMENTS

The experiments evaluate whether Grasp-Anything is challenging for grasp detection and whether foundation-model-generated data transfers to real robot experiments.

IV. EXPERIMENTS

  • The experiments address dataset difficulty for grasp detection and transferability to real robot experiments.Source: §IV · p. 5

A. Zero-shot Grasp Detection

Zero-shot experiments evaluate three grasp networks across five datasets using base-to-new, cross-dataset, and generalized zero-shot settings. Grasp-Anything is more challenging on unseen objects but improves transfer and generalized zero-shot performance.

Base-to-New Generalization

Cross-dataset Transfer Learning

Generalized Zero-shot Learning

B. Robotic Evaluation

The robotic evaluation uses GR-ConvNet with depth-based 6DOF grasp conversion and trajectory planning on single-object and cluttered scenes. Models trained on Grasp-Anything achieve the highest reported success rates.

Setup

  • The evaluation runs 60 trials per scenario across one object and a cluttered set of 15 objects.Source: B. Robotic Evaluation · p. 5
  • Grasp detections are converted into 6DOF grasp poses from RealSense depth images and executed using a trajectory planner.Source: B. Robotic Evaluation · p. 5

Results

C. Grasp Detection in the Wild

Qualitative evaluations show improved grasp detection quality when the same GR-ConvNet is trained on Grasp-Anything. A pretrained model also produces adequate grasp poses on images from other datasets and the internet.

D. Discussion

The discussion links Grasp-Anything’s zero-shot and robotic gains to broad coverage of unseen objects and natural scene arrangements. The conclusion presents it as a foundation for language-driven grasp detection, while noting costly construction and missing 3D point clouds.

D. Discussion

  • Robotic experiments show the synthesis-trained model outperforming models trained on other datasets, supporting real-world usefulness.Source: D. Discussion · p. 6
  • Grasp-Anything supports challenging zero-shot grasp detection because its broader coverage lowers accuracy on unseen-object testing while improving transfer elsewhere.Source: D. Discussion · p. 6
  • Dataset creation took approximately three months for 1M scene descriptions and depends on access to the commercial ChatGPT API.Source: D. Discussion · p. 6
  • The dataset currently lacks 3D point clouds because available text-to-point-cloud and image-to-point-cloud foundation models were not convincing.Source: D. Discussion · p. 6

V. CONCLUSIONS

  • The authors position Grasp-Anything as a foundation dataset for language-driven grasp detection through natural scene descriptions.Source: §V · p. 6

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