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This dataset is released under the Pantheon Commercial Use License, Version 1.0 (see LICENSE.md). It may be used for any lawful purpose, including commercial use and training models, subject to the license. You may not sell or redistribute the data itself for a fee, and you may not attempt to identify anyone in it.
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Pantheon DiverseUMI (100h Sample), annotations
Project page: pantheon.inc/diverse-umi
Data preview viewer: pantheon.inc/100-data-board
Pantheon DiverseUMI (100h Sample) contains 99.6 hours of bimanual tabletop manipulation, recorded with two handheld UMI grippers and a fisheye camera above the table (a 360° dual-fisheye camera in some recordings). It has 12,219 episodes from 1,134 recordings. Each episode is one task, such as stacking two blocks or passing a ball between the grippers. Every episode has three synchronized videos (the top camera and both wrist cameras), the pose and actions of both wrists at every frame, and dense labels of what the operator does.
Operators worked freely with everyday objects. Tasks in one recording can overlap in time, so 156 pairs of episodes share some frames. Of the 12,219 tasks, 11,563 succeed, 441 fail and 215 are partially completed. Episodes run from 1s to 1031s, with a median of 10s. The tasks cover 179 skills, led by pick and place (5,826), stack (894), hand over (528) and stand up (487).
This dataset holds the labels. The videos, poses and actions of every episode are in DiverseUMI-sample100.
Full dataset and collaborations
For access to more of DiverseUMI through a research or data collaboration, reach out to data@pantheon.inc. We work with both commercial and academic labs.
License
This dataset is released under the Pantheon Commercial Use License, Version 1.0. In short:
- You may use it for any lawful purpose, including commercial products and research.
- You may train, fine-tune and evaluate models on it. You own the models you train and what they generate, and you can deploy, release or sell them on your own terms.
- You may not sell, rent or license the data itself, or anything that reproduces it, or offer it as a dataset or data product.
- You may not use it to build a dataset or data product that competes with this dataset or with Pantheon's data products. Training and selling models is not affected.
- You may not try to identify anyone in the videos, extract biometric data, or combine the data with other data in a way that could identify someone.
If you share the data or a copy derived from it (not a trained model), include the license and this attribution:
Pantheon DiverseUMI (100h Sample), by Pantheon Industries Inc., licensed under the Pantheon Commercial Use License, Version 1.0. https://huggingface.co/datasets/Pantheon-inc/DiverseUMI-sample100-annotations
Technical details
Expand each section for the files and every field of the labels.
Files
labels.jsonl one line per episode: episode_id, recording_id and labels, the episode's labels.json (below)
vocabulary.json for some label fields, other spellings of a value and the one spelling the labels use
episode_id matches the episode_id in meta/episode_info.jsonl of DiverseUMI-sample100, which gives the episode's episode_index there, and the labels describe that episode's videos and features. The Labels section gives the clock of every time in them.
Labels
The labels were written by a vision-language model that watched all three videos at 2 frames per second, together
with the gripper opening measured from the wrist cameras, so their times are accurate to about half a second. Times
are in seconds from the start of the episode, except inside _source and in fields whose name ends in
_at_recording_s, which are in seconds from the start of the recording. For event kinds, mistake categories, skills and actions,
the labels use one spelling of a value the model wrote in several ways, and vocabulary.json maps each of those
other spellings to it. Related fields can name the same thing differently, such as a drop key event and
a dropped_object mistake. A key the model had nothing to write for is null or left out, so an item can lack a value for
any of the keys listed below.
| Field | Meaning |
|---|---|
task |
what the operator does in this episode |
skill, actions |
the task's main skill, and the other actions in it |
completion |
outcome (success, failure or partial), success_predicate (what counts as done), completed_at_s (when it was met, null unless the task succeeded) and reason (the model's explanation of the outcome, often null, and sometimes present for a success when the model had a doubt) |
timeline |
the steps of the task, each with start_s, end_s, arm (left, right or both), action, object, destination, spatial_relation, contribution (advancing, wasteful or idle), progress and sometimes notes |
key_events |
moments, each with t_s, kind, label, outcome (success, failure or unclear) and sometimes a note |
state_changes |
an object changing state, with t_s, object, from, to, seen_in (which cameras show it, in words; the top camera is called exo) and sometimes a note |
scene_graph |
the spatial relations between objects at time t_s, as short phrases |
recovery |
a failure (failure, failure_t_s), and whether and how the operator recovered from it (recovered, recovered_at_s, correction, how_to_fix, and sometimes a note). When the fix comes only after the episode ends, recovered is false and the fix is in recovered_after_the_episode_at_recording_s and, when the model described it, correction_after_the_episode |
operator_mistakes |
a mistake from t_s to end_s, with issue, evidence, category, severity (low, medium or high), deliberate (yes, no or unclear: whether the operator meant to do it) and recovered (whether the operator recovered within the episode) |
idle_stretches |
a stretch from t_s to end_s where the operator does not work on the task, with category (idle_stretch), issue, evidence and severity |
_source |
labels about the whole recording, and events that touch this episode but belong elsewhere (below) |
progress is the model's estimate of how much of the task is done at the end of a step, from 0 to 1. It can fall after
a failure or on an idle step after the task is done, and it does not always agree with the outcome: 31 failed tasks reach 1.0, and 46 successful
tasks never do.
_source holds what is not about this episode alone: labels about the whole recording, the objects of this episode's
task, and events and text from the recording that touch this episode but belong elsewhere. Every time in it is in
seconds from the start of the recording. start_in_recording_s in meta/episode_info.jsonl of DiverseUMI-sample100 converts between the two clocks.
| Field | Meaning |
|---|---|
recording_summary |
summaries of the whole recording, sometimes several that overlap |
recording_review |
assessments of the operator's work over the whole recording, sometimes several joined together |
recording_instruction_variants |
instructions that describe the recording, which often name other tasks |
task_objects |
the objects of this task |
scene_at_recording_start |
the setting, and the objects the model listed for the recording, with name, attributes and location. An object can appear under more than one name |
data_issues |
issues about the recording, such as repeated idle pauses, with issue, category, severity and, when the model gave them, evidence; one with t_s and end_s covers that stretch of the recording, and one without them concerns the recording as a whole |
key_events, state_changes, recovery, operator_mistakes |
events from the recording that touch this episode but belong elsewhere: they started before the episode, they start at its end, or they belong to another task that overlaps it, which belongs_to_task names (that task may have no episode of its own, and may be the task that ends where this |
| episode starts). An event in frames that two episodes share can stay in both | |
text_beyond_the_episode |
label text about moments outside the episode, with the list and field of the label it was written in (that label may no longer be in this episode), and entry_time_in_recording_s when its entry had a time |
People in the videos
The wrist and top cameras show the operators, and sometimes people passing by. Faces are not blurred. The license forbids trying to identify anyone, extracting biometric data, and publishing images in which a person could be identified (Article 7).
Loading the labels
import json
from huggingface_hub import hf_hub_download
labels = {r["episode_id"]: r["labels"] for r in map(json.loads, open(hf_hub_download("Pantheon-inc/DiverseUMI-sample100-annotations", "labels.jsonl", repo_type="dataset")))}
ep = labels["<episode_id>"]
print(ep["task"], ep["completion"]["outcome"])
for step in ep["timeline"][:10]:
print(step["start_s"], step["end_s"], step["arm"], step["action"], step["progress"])
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