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| Method | ckpt | Camera | Seen (AP) | Similar (AP) | Novel (AP) | Average (AP) |
|---|---|---|---|---|---|---|
| FineGrasp | finegrasp_pipeline/model.safetensors | Realsense | 71.67 | 62.83 | 27.40 | 53.97 |
| FineGrasp + CD | finegrasp_pipeline/model.safetensors | Realsense | 73.71 | 64.56 | 28.14 | 55.47 |
| FineGrasp + Simulation Data | finegrasp_pipeline_sim/model.safetensors | Realsense | 70.21 | 61.98 | 26.18 | 52.79 |
Notice: finegrasp_pipeline_sim/model.safetensors is trained for Challenge Cup RoboTwin simulation benchmark.
1import os
2import numpy as np
3import scipy.io as scio
4from PIL import Image
5from robo_orchard_lab.models.finegrasp.processor import GraspInput
6from huggingface_hub import snapshot_download
7from robo_orchard_lab.inference import InferencePipelineMixin
8
9file_path = snapshot_download(
10 repo_id="HorizonRobotics/FineGrasp",
11 allow_patterns=[
12 "finegrasp_pipeline/**",
13 "data_example/**"
14 ],
15)
16
17loaded_pipeline = InferencePipelineMixin.load(
18 os.path.join(file_path, "finegrasp_pipeline")
19)
20
21rgb_image_path = os.path.join(file_path, "data_example/0000_rgb.png")
22depth_image_path = os.path.join(file_path, "data_example/0000_depth.png")
23intrinsic_file = os.path.join(file_path, "data_example/0000.mat")
24
25depth_image = np.array(Image.open(depth_image_path), dtype=np.float32)
26rgb_image = np.array(Image.open(rgb_image_path), dtype=np.float32) / 255.0
27intrinsic_matrix = scio.loadmat(intrinsic_file)["intrinsic_matrix"]
28workspace = [-1, 1, -1, 1, 0.0, 2.0]
29depth_scale = 1000.0
30
31input_data = GraspInput(
32 rgb_image=rgb_image,
33 depth_image=depth_image,
34 depth_scale=depth_scale,
35 intrinsic_matrix=intrinsic_matrix,
36 workspace=workspace,
37)
38
39loaded_pipeline.to("cuda")
40loaded_pipeline.model.eval()
41output = loaded_pipeline(input_data)
42print(f"Best grasp pose: {output.grasp_poses[0]}")
43@misc{du2025finegrasp,
title={FineGrasp: Towards Robust Grasping for Delicate Objects},
author={Yun Du and Mengao Zhao and Tianwei Lin and Yiwei Jin and Chaodong Huang and Zhizhong Su},
year={2025},
eprint={2507.05978},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2507.05978},
}