Revisiting Embodied Chain-of-Thought for Generalizable Robot Manipulation
Paper • 2606.03784 • Published
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json dict | __key__ string | __url__ string |
|---|---|---|
{"task_name":"episode_0000000","episode_json":"json/train_0_100/chunk-0000/episode_0000000.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000000_ecot | hf://datasets/ERVLA/AgiBot@b39abf804ba3228f18cbb6ef976e2fc3f209ad55/archives/cot-00000.tar.zst |
{"task_name":"episode_0000001","episode_json":"json/train_0_100/chunk-0000/episode_0000001.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000001_ecot | hf://datasets/ERVLA/AgiBot@b39abf804ba3228f18cbb6ef976e2fc3f209ad55/archives/cot-00000.tar.zst |
{"task_name":"episode_0000002","episode_json":"json/train_0_100/chunk-0000/episode_0000002.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000002_ecot | hf://datasets/ERVLA/AgiBot@b39abf804ba3228f18cbb6ef976e2fc3f209ad55/archives/cot-00000.tar.zst |
{"task_name":"episode_0000003","episode_json":"json/train_0_100/chunk-0000/episode_0000003.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000003_ecot | hf://datasets/ERVLA/AgiBot@b39abf804ba3228f18cbb6ef976e2fc3f209ad55/archives/cot-00000.tar.zst |
{"task_name":"episode_0000004","episode_json":"json/train_0_100/chunk-0000/episode_0000004.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000004_ecot | hf://datasets/ERVLA/AgiBot@b39abf804ba3228f18cbb6ef976e2fc3f209ad55/archives/cot-00000.tar.zst |
{"task_name":"episode_0000005","episode_json":"json/train_0_100/chunk-0000/episode_0000005.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000005_ecot | hf://datasets/ERVLA/AgiBot@b39abf804ba3228f18cbb6ef976e2fc3f209ad55/archives/cot-00000.tar.zst |
{"task_name":"episode_0000006","episode_json":"json/train_0_100/chunk-0000/episode_0000006.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000006_ecot | hf://datasets/ERVLA/AgiBot@b39abf804ba3228f18cbb6ef976e2fc3f209ad55/archives/cot-00000.tar.zst |
{"task_name":"episode_0000007","episode_json":"json/train_0_100/chunk-0000/episode_0000007.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000007_ecot | hf://datasets/ERVLA/AgiBot@b39abf804ba3228f18cbb6ef976e2fc3f209ad55/archives/cot-00000.tar.zst |
{"task_name":"episode_0000008","episode_json":"json/train_0_100/chunk-0000/episode_0000008.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000008_ecot | hf://datasets/ERVLA/AgiBot@b39abf804ba3228f18cbb6ef976e2fc3f209ad55/archives/cot-00000.tar.zst |
{"task_name":"episode_0000009","episode_json":"json/train_0_100/chunk-0000/episode_0000009.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000009_ecot | hf://datasets/ERVLA/AgiBot@b39abf804ba3228f18cbb6ef976e2fc3f209ad55/archives/cot-00000.tar.zst |
End of preview.
ERVLA AgiBot
AgiBot demonstrations with embodied chain-of-thought annotations for ERVLA. The demonstrations originate from AgiBot World.
| Content | Files |
|---|---|
| Episode JSON | 776,158 |
| CoT JSON | 776,158 |
| Videos | 2,327,916 |
The dataset is distributed as independent tar.zst archives in archives/.
All archives extract into the same dataset root:
json/
cot/
videos/
Paths in JSON and CoT are relative to this root. Archives contain the video
contents, with no symlinks or machine-specific ownership metadata.
Download every archive to restore the full dataset; individual archives may
contain only one content type. archives.json lists available archives and
their checksums. Its complete field becomes true after all archives have
been uploaded.
hf download ERVLA/AgiBot --repo-type dataset --local-dir agibot
cd agibot
sha256sum -c SHA256SUMS
for archive in archives/*.tar.zst; do
tar --zstd -xf "$archive"
done
Extraction requires GNU tar and Zstandard (zstd).
End-effector projections were refreshed where camera calibration was available; 2,145 CoT files retain their original projections.
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