Search is not available for this dataset
sample_id int64 0 499 | start_x int64 0 9 | start_y int64 0 9 | goal_x int64 0 9 | goal_y int64 0 9 | manhattan_distance int64 1 17 | path_length int64 1 25 | efficiency float64 0.56 1 | n_obstacles int64 3 9 | grid_size int64 10 10 |
|---|---|---|---|---|---|---|---|---|---|
0 | 6 | 3 | 7 | 4 | 2 | 2 | 1 | 9 | 10 |
1 | 8 | 0 | 9 | 2 | 3 | 5 | 0.6 | 6 | 10 |
2 | 1 | 9 | 8 | 9 | 7 | 10 | 0.7 | 7 | 10 |
3 | 1 | 9 | 3 | 7 | 4 | 4 | 1 | 8 | 10 |
4 | 2 | 0 | 7 | 2 | 7 | 7 | 1 | 5 | 10 |
5 | 6 | 6 | 7 | 4 | 3 | 3 | 1 | 5 | 10 |
6 | 2 | 6 | 0 | 3 | 5 | 8 | 0.625 | 7 | 10 |
7 | 9 | 6 | 8 | 6 | 1 | 1 | 1 | 8 | 10 |
8 | 5 | 7 | 8 | 3 | 7 | 8 | 0.875 | 3 | 10 |
9 | 0 | 7 | 0 | 0 | 7 | 9 | 0.778 | 4 | 10 |
10 | 9 | 5 | 6 | 3 | 5 | 8 | 0.625 | 9 | 10 |
11 | 3 | 6 | 3 | 8 | 2 | 2 | 1 | 3 | 10 |
12 | 6 | 9 | 2 | 6 | 7 | 10 | 0.7 | 6 | 10 |
13 | 2 | 3 | 7 | 5 | 7 | 10 | 0.7 | 3 | 10 |
14 | 2 | 8 | 1 | 1 | 8 | 9 | 0.889 | 4 | 10 |
15 | 7 | 7 | 6 | 2 | 6 | 10 | 0.6 | 3 | 10 |
16 | 2 | 5 | 7 | 1 | 9 | 13 | 0.692 | 7 | 10 |
17 | 0 | 3 | 4 | 6 | 7 | 12 | 0.583 | 3 | 10 |
18 | 7 | 1 | 5 | 6 | 7 | 9 | 0.778 | 4 | 10 |
19 | 9 | 6 | 9 | 2 | 4 | 5 | 0.8 | 4 | 10 |
20 | 1 | 1 | 6 | 5 | 9 | 12 | 0.75 | 5 | 10 |
21 | 4 | 4 | 6 | 3 | 3 | 5 | 0.6 | 8 | 10 |
22 | 6 | 8 | 4 | 0 | 10 | 17 | 0.588 | 4 | 10 |
23 | 6 | 4 | 5 | 6 | 3 | 4 | 0.75 | 5 | 10 |
24 | 4 | 6 | 3 | 0 | 7 | 12 | 0.583 | 8 | 10 |
25 | 1 | 0 | 3 | 7 | 9 | 9 | 1 | 6 | 10 |
26 | 1 | 2 | 6 | 0 | 7 | 10 | 0.7 | 4 | 10 |
27 | 7 | 3 | 8 | 4 | 2 | 3 | 0.667 | 7 | 10 |
28 | 4 | 0 | 6 | 6 | 8 | 10 | 0.8 | 3 | 10 |
29 | 2 | 0 | 3 | 8 | 9 | 13 | 0.692 | 5 | 10 |
30 | 3 | 4 | 6 | 8 | 7 | 11 | 0.636 | 9 | 10 |
31 | 9 | 1 | 4 | 4 | 8 | 10 | 0.8 | 8 | 10 |
32 | 2 | 1 | 9 | 3 | 9 | 15 | 0.6 | 6 | 10 |
33 | 4 | 5 | 4 | 4 | 1 | 1 | 1 | 6 | 10 |
34 | 9 | 7 | 5 | 7 | 4 | 4 | 1 | 6 | 10 |
35 | 3 | 2 | 5 | 1 | 3 | 4 | 0.75 | 5 | 10 |
36 | 1 | 0 | 4 | 9 | 12 | 20 | 0.6 | 9 | 10 |
37 | 0 | 4 | 8 | 0 | 12 | 16 | 0.75 | 5 | 10 |
38 | 2 | 1 | 0 | 5 | 6 | 8 | 0.75 | 6 | 10 |
39 | 1 | 2 | 1 | 3 | 1 | 1 | 1 | 3 | 10 |
40 | 3 | 9 | 7 | 0 | 13 | 16 | 0.812 | 4 | 10 |
41 | 2 | 8 | 6 | 6 | 6 | 6 | 1 | 9 | 10 |
42 | 8 | 9 | 1 | 0 | 16 | 25 | 0.64 | 8 | 10 |
43 | 7 | 1 | 9 | 7 | 8 | 10 | 0.8 | 5 | 10 |
44 | 9 | 6 | 8 | 0 | 7 | 9 | 0.778 | 9 | 10 |
45 | 4 | 5 | 3 | 9 | 5 | 5 | 1 | 4 | 10 |
46 | 6 | 3 | 7 | 6 | 4 | 7 | 0.571 | 3 | 10 |
47 | 5 | 1 | 3 | 3 | 4 | 5 | 0.8 | 9 | 10 |
48 | 0 | 0 | 9 | 5 | 14 | 14 | 1 | 7 | 10 |
49 | 8 | 7 | 5 | 6 | 4 | 6 | 0.667 | 5 | 10 |
50 | 5 | 7 | 8 | 4 | 6 | 8 | 0.75 | 8 | 10 |
51 | 0 | 6 | 0 | 8 | 2 | 2 | 1 | 3 | 10 |
52 | 5 | 2 | 6 | 2 | 1 | 1 | 1 | 4 | 10 |
53 | 6 | 0 | 3 | 5 | 8 | 11 | 0.727 | 4 | 10 |
54 | 7 | 4 | 3 | 1 | 7 | 8 | 0.875 | 5 | 10 |
55 | 6 | 9 | 9 | 7 | 5 | 8 | 0.625 | 8 | 10 |
56 | 6 | 0 | 4 | 4 | 6 | 8 | 0.75 | 4 | 10 |
57 | 4 | 8 | 9 | 3 | 10 | 16 | 0.625 | 3 | 10 |
58 | 5 | 1 | 6 | 1 | 1 | 1 | 1 | 5 | 10 |
59 | 0 | 5 | 9 | 1 | 13 | 13 | 1 | 7 | 10 |
60 | 7 | 2 | 1 | 0 | 8 | 13 | 0.615 | 8 | 10 |
61 | 1 | 0 | 6 | 6 | 11 | 18 | 0.611 | 6 | 10 |
62 | 2 | 2 | 0 | 7 | 7 | 9 | 0.778 | 4 | 10 |
63 | 6 | 1 | 6 | 9 | 8 | 10 | 0.8 | 9 | 10 |
64 | 8 | 1 | 4 | 2 | 5 | 7 | 0.714 | 6 | 10 |
65 | 8 | 6 | 8 | 5 | 1 | 1 | 1 | 8 | 10 |
66 | 6 | 4 | 9 | 5 | 4 | 6 | 0.667 | 7 | 10 |
67 | 2 | 4 | 3 | 2 | 3 | 4 | 0.75 | 4 | 10 |
68 | 7 | 5 | 5 | 4 | 3 | 3 | 1 | 3 | 10 |
69 | 1 | 8 | 9 | 1 | 15 | 15 | 1 | 7 | 10 |
70 | 2 | 5 | 1 | 2 | 4 | 6 | 0.667 | 7 | 10 |
71 | 7 | 8 | 1 | 8 | 6 | 6 | 1 | 6 | 10 |
72 | 4 | 6 | 8 | 2 | 8 | 8 | 1 | 4 | 10 |
73 | 7 | 2 | 1 | 2 | 6 | 6 | 1 | 7 | 10 |
74 | 9 | 1 | 6 | 1 | 3 | 4 | 0.75 | 5 | 10 |
75 | 0 | 0 | 2 | 7 | 9 | 16 | 0.562 | 8 | 10 |
76 | 7 | 1 | 8 | 6 | 6 | 10 | 0.6 | 6 | 10 |
77 | 7 | 5 | 4 | 4 | 4 | 5 | 0.8 | 8 | 10 |
78 | 8 | 8 | 3 | 4 | 9 | 14 | 0.643 | 4 | 10 |
79 | 1 | 7 | 5 | 7 | 4 | 6 | 0.667 | 5 | 10 |
80 | 7 | 7 | 1 | 7 | 6 | 6 | 1 | 5 | 10 |
81 | 5 | 6 | 4 | 9 | 4 | 4 | 1 | 3 | 10 |
82 | 3 | 1 | 6 | 1 | 3 | 3 | 1 | 7 | 10 |
83 | 3 | 3 | 3 | 1 | 2 | 3 | 0.667 | 6 | 10 |
84 | 3 | 6 | 0 | 5 | 4 | 7 | 0.571 | 9 | 10 |
85 | 7 | 4 | 4 | 3 | 4 | 4 | 1 | 8 | 10 |
86 | 7 | 1 | 3 | 6 | 9 | 13 | 0.692 | 5 | 10 |
87 | 8 | 5 | 5 | 4 | 4 | 6 | 0.667 | 7 | 10 |
88 | 2 | 7 | 8 | 0 | 13 | 22 | 0.591 | 6 | 10 |
89 | 0 | 3 | 9 | 5 | 11 | 19 | 0.579 | 9 | 10 |
90 | 3 | 6 | 3 | 3 | 3 | 5 | 0.6 | 8 | 10 |
91 | 4 | 6 | 2 | 1 | 7 | 11 | 0.636 | 8 | 10 |
92 | 3 | 4 | 3 | 3 | 1 | 1 | 1 | 6 | 10 |
93 | 6 | 9 | 2 | 2 | 11 | 17 | 0.647 | 4 | 10 |
94 | 6 | 9 | 1 | 6 | 8 | 10 | 0.8 | 7 | 10 |
95 | 2 | 4 | 4 | 0 | 6 | 8 | 0.75 | 6 | 10 |
96 | 0 | 8 | 5 | 3 | 10 | 10 | 1 | 6 | 10 |
97 | 4 | 3 | 9 | 0 | 8 | 10 | 0.8 | 9 | 10 |
98 | 1 | 6 | 5 | 3 | 7 | 9 | 0.778 | 9 | 10 |
99 | 1 | 1 | 0 | 9 | 9 | 9 | 1 | 9 | 10 |
End of preview. Expand in Data Studio
ACCESS REQUIREMENT - FOLLOW TO DOWNLOAD
This dataset requires following the author to access.
How to Access
- Follow @shangshang on HuggingFace: https://huggingface.co/shangshang
- Request access by commenting on the dataset page
- Once approved, you will receive download permissions
Usage Agreement
- For research and educational purposes only
- Do not redistribute without permission
- Cite the dataset in your work:
@misc{shangshang_dataset_2026,
title={Embodied AI and Medical Datasets},
year={2026},
url={https://huggingface.co/datasets/shangshang}
}
Embodied AI Dataset Collection
A comprehensive dataset for Vision-Language-Action (VLA) model training and embodied AI research.
Datasets Included
| Dataset | Samples | Description |
|---|---|---|
| robot_trajectory.csv | 1000 | Robot arm trajectories for various manipulation tasks |
| joint_configurations.csv | 800 | 6-DOF joint angle configurations |
| end_effector_poses.csv | 600 | End-effector pose sequences |
| force_tactile.csv | 500 | Force/tactile sensor feedback |
| language_instructions.csv | 1000 | Natural language task instructions |
| vla_combined_dataset.csv | 500 | Combined VLA training data |
Task Categories
- pick_place: Pick up object and place at target
- push_pull: Push/pull objects
- stacking: Stack objects
- insertion: Insert objects into targets
- turning: Turn knobs/dials
Robot Types
- Franka Emika (7-DOF)
- UR5e (6-DOF)
- WidowX 250 (7-DOF)
- xArm6 (6-DOF)
Usage Example
import pandas as pd
from huggingface_hub import hf_hub_download
# Download
file_path = hf_hub_download(
repo_id="shangshang/embodied-ai-dataset",
filename="vla_combined_dataset.csv"
)
# Load
df = pd.read_csv(file_path)
print(df.head())
License
MIT License
Citation
If you use this dataset in your research, please cite:
@misc{embodied_ai_dataset_2026, title={Embodied AI VLA Dataset}, year={2026}, url={https://huggingface.co/datasets/shangshang/embodied-ai-dataset} }
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