Datasets:

shangshang
/

robot-manip

sample_id int64	object_type string	source_x float64	source_y float64	target_x float64	target_y float64	distance float64	success int64
0	cup	0.09	0.434	0.356	0.516	0.279	1
1	toy	0.543	0.305	0.182	0.086	0.423	1
2	box	0.188	0.653	0.148	0.554	0.106	1
3	toy	0.31	0.664	0.666	0.286	0.519	1
4	box	0.432	0.818	0.843	0.52	0.507	0
5	cup	0.28	0.127	0.639	0.075	0.363	1
6	bottle	0.956	0.772	0.842	0.761	0.114	1
7	box	0.116	0.536	0.437	0.268	0.418	1
8	cup	0.094	0.291	0.984	0.961	1.114	1
9	cup	0.846	0.611	0.04	0.901	0.856	1
10	cup	0.834	0.833	0.698	0.291	0.558	1
11	cup	0.583	0.699	0.854	0.14	0.621	1
12	box	0.447	0.343	0.138	0.439	0.323	1
13	box	0.679	0.421	0.961	0.599	0.334	0
14	box	0.337	0.569	0.861	0.078	0.718	1
15	box	0.867	0.953	0.405	0.506	0.643	0
16	cup	0.13	0.557	0.946	0.415	0.829	1
17	book	0.561	0.186	0.224	0.494	0.457	0
18	book	0.609	0.821	0.285	0.239	0.666	0
19	bottle	0.325	0.721	0.658	0.959	0.409	0
20	box	0.613	0.585	0.548	0.64	0.085	1
21	box	0.119	0.225	0.5	0.84	0.724	0
22	toy	0.827	0.831	0.292	0.875	0.536	1
23	toy	0.989	0.167	0.047	0.78	1.124	1
24	toy	0.704	0.946	0.409	0.662	0.409	1
25	box	0.45	0.189	0.508	0.529	0.345	0
26	bottle	0.631	0.35	0.293	0.393	0.341	1
27	cup	0.821	0.038	0.367	0.542	0.678	1
28	book	0.298	0.412	0.224	0.933	0.526	1
29	bottle	0.705	0.672	0.394	0.001	0.739	1
30	cup	0.489	0.383	0.222	0.825	0.516	1
31	toy	0.192	0.84	0.725	0.895	0.536	1
32	bottle	0.81	0.374	0.231	0.735	0.682	1
33	bottle	0.177	0.58	0.904	0.32	0.772	1
34	cup	0.211	0.313	0.179	0.902	0.59	1
35	bottle	0.039	0.639	0.051	0.926	0.288	0
36	box	0.861	0.566	0.157	0.691	0.715	1
37	book	0.373	0.146	0.406	0.113	0.047	1
38	box	0.603	0.326	0.984	0.667	0.511	1
39	book	0.623	0.056	0.024	0.45	0.717	1
40	book	0.356	0.213	0.756	0.64	0.585	1
41	cup	0.32	0.592	0.072	0.147	0.509	1
42	box	0.812	0.808	0.19	0.11	0.935	0
43	cup	0.642	0.038	0.621	0.778	0.74	1
44	cup	0.671	0.031	0.726	0.836	0.806	1
45	box	0.77	0.561	0.883	0.528	0.118	1
46	book	0.934	0.523	0.292	0.267	0.692	0
47	bottle	0.53	0.615	0.482	0.094	0.523	0
48	bottle	0.169	0.621	0.719	0.57	0.552	1
49	bottle	0.882	0.582	0.127	0.462	0.765	0
50	toy	0.265	0.711	0.1	0.034	0.697	1
51	toy	0.796	0.34	0.007	0.48	0.801	1
52	cup	0.033	0.367	0.237	0.447	0.219	1
53	box	0.145	0.695	0.142	0.64	0.056	1
54	toy	0.931	0.005	0.748	0.903	0.916	1
55	bottle	0.708	0.463	0.806	0.18	0.3	0
56	cup	0.81	0.088	0.634	0.915	0.846	1
57	toy	0.58	0.163	0.674	0.133	0.099	1
58	bottle	0.463	0.599	0.171	0.742	0.325	1
59	cup	0.129	0.446	0.815	0.889	0.818	1
60	book	0.748	0.262	0.882	0.892	0.644	1
61	box	0.392	0.713	0.659	0.625	0.282	1
62	bottle	0.103	0.719	0.504	0.709	0.401	1
63	box	0.334	0.026	0.341	0.803	0.778	0
64	box	0.933	0.632	0.288	0.638	0.644	1
65	bottle	0.852	0.5	0.679	0.392	0.205	1
66	cup	0.429	0.652	0.727	0.001	0.717	0
67	cup	0.234	0.263	0.038	0.123	0.241	0
68	box	0.318	0.381	0.991	0.248	0.686	1
69	bottle	0.021	0.223	0.057	0.103	0.125	1
70	box	0.587	0.624	0.653	0.07	0.558	1
71	book	0.381	0.412	0.728	0.452	0.349	0
72	bottle	0.184	0.437	0.392	0.405	0.211	1
73	toy	0.036	0.528	0.662	0.207	0.703	0
74	cup	0.471	0.411	0.237	0.73	0.396	1
75	box	0.615	0.832	0.494	0.118	0.724	1
76	book	0.698	0.79	0.296	0.496	0.499	1
77	box	0.661	0.882	0.409	0.29	0.643	1
78	toy	0.402	0.375	0.183	0.43	0.226	0
79	cup	0.008	0.839	0.032	0.94	0.104	0
80	book	0.871	0.015	0.436	0.224	0.483	1
81	book	0.457	0.55	0.888	0.105	0.619	0
82	bottle	0.945	0.482	0.571	0.826	0.508	1
83	bottle	0.442	0.491	0.849	0.706	0.461	1
84	box	0.885	0.577	0.221	0.269	0.732	0
85	toy	0.045	0.31	0.292	0.811	0.559	1
86	bottle	0.669	0.732	0.432	0.68	0.243	1
87	cup	0.681	0.121	0.114	0.411	0.637	1
88	toy	0.972	0.038	0.316	0.314	0.712	1
89	book	0.863	0.773	0.169	0.822	0.696	0
90	box	0.977	0.037	0.445	0.113	0.538	1
91	box	0.359	0.475	0.364	0.641	0.166	0
92	cup	0.145	0.904	0.969	0.992	0.828	0
93	bottle	0.279	0.95	0.063	0.603	0.409	1
94	bottle	0.826	0.914	0.88	1	0.101	1
95	toy	0.315	0.129	0.468	0.598	0.494	1
96	box	0.688	0.604	0.936	0.234	0.445	1
97	box	0.162	0.906	0.516	0.154	0.832	0
98	box	0.018	0.271	0.805	0.615	0.859	0
99	bottle	0.22	0.439	0.782	0.784	0.66	1

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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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