unitreerobotics/G1_Dex1_Stack_Block, measured from the public release on 13 September 2026. frame level, every row read
Nobody opens a robot dataset and watches all of it. We read every frame of every episode and report what is in there.
Three of this release's 35 action dimensions are the body channels, and they hold one single value across all 200 episodes. The policy is asked to predict those three from a constant, which costs it capacity and teaches it nothing.
episode 0 of unitreerobotics/G1_Dex1_Stack_Block, cut from the release's own video, 16 seconds. Nothing is re-rendered or sped up.
We read every frame of unitreerobotics/G1_Dex1_Stack_Block, 200 episodes and 93,131 rows, and 3 of its 35 action dimensions hold one value from the first frame to the last, the ones named body[0], body[1], body[2]. A column that never changes is one the policy is being asked to predict from a constant, so it costs capacity and teaches nothing.
| property | value |
|---|---|
| episodes | 200 |
| frames | 93,131 |
| frame rate | 30 fps |
| robot | G1_ALL |
| action dimensions | 35 |
| cameras | 4 |
| distinct task strings | |
| shortest episode | not declared |
| median episode | not declared |
| longest episode | not declared |
A trailing hold is the number of frames at the end during which the commanded action does not change by more than 1e-4 on any component. Seconds come from the frame rate the release declares.
| trailing hold at least | episodes | share |
|---|---|---|
| 1 frames , 0.03 seconds | 0 | 0.0% |
| 5 frames , 0.17 seconds | 0 | 0.0% |
| 10 frames , 0.33 seconds | 0 | 0.0% |
| 30 frames , 1.0 seconds | 0 | 0.0% |
| 60 frames , 2.0 seconds | 0 | 0.0% |
The release is a meta directory plus one or more parquet files. Read data/chunk-000/file-000.parquet, group rows by the episode ranges in meta/episodes, and compute the same columns. No credentials.
This page is what a published release will tell you on its own. The full grading removes the part that scores worst and retrains on what is left, then reports whether the smaller corpus scores higher, with rollout counts and intervals. The one result measured on physical hardware is Stera-10M, two arms on a four arm SO-ARM101 bench, 21.9% against 36.9% task success across 1,270 rollouts.