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.
Questions: data@pantheon.inc

Pantheon DiverseUMI (25h Sample)

This dataset holds the videos, poses and actions of every episode, and their labels are in their own dataset, DiverseUMI-sample25-annotations.

Project page: pantheon.inc/diverse-umi

Pantheon DiverseUMI (25h Sample) contains 25.0 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 4,587 episodes from 293 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 73 pairs of episodes share some frames. Of the 4,587 tasks, 4,390 succeed, 154 fail and 43 are partially completed. Episodes run from 1s to 609s, with a median of 7s. The tasks cover 77 skills, led by pick and place (2,669), stack (320), hand over (237) and stand up (224).

Changelog

Every update to the dataset is listed in the CHANGELOG.

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 (25h Sample), by Pantheon Industries Inc., licensed under the Pantheon Commercial Use License, Version 1.0. https://huggingface.co/datasets/Pantheon-inc/DiverseUMI-sample25

Technical details

Expand each section for the files, the cameras, every field, and how the actions were made.

Files

freeform/<episode_id>/
  exo.mp4         top camera (called exo in the labels)
  left.mp4        left wrist camera
  right.mp4       right wrist camera
  actions.npz     pose, actions and validity of both wrists at every frame
  meta.json       the episode's task, outcome, cameras and place in its recording
manifest.jsonl    the meta.json of every episode, one per line

All three videos run at 30 fps and start on the same frame. Frame k of each video is row k of the per-frame arrays in actions.npz (length N), and row k of the per-step arrays (length N-1) is the step from frame k to k+1. Every episode is in freeform/ and is named <recording_id>__t<NN>, where NN is the task's index in its recording (two or more digits).

The labels are in DiverseUMI-sample25-annotations, one line per episode with its episode_id.

Cameras

The wrist videos are 640 x 480. The top camera records a fisheye image, and exo.mp4 is a flat (rectilinear) view of the table taken from it. Its size depends on the camera and how it was mounted:

exo.mp4 size	Episodes	Source
746 x 480	2,250	fisheye source
746 x 480	2,161	dual fisheye source
696 x 448	176	dual fisheye source

exo_view in meta.json gives the exact view of every episode, so it can be reproduced from the fisheye recording:

Key	Meaning
`source_lens`, `source_fov_deg`	the top camera's lens (fisheye, or dual fisheye for a 360° camera) and the field of view of one lens
`projection`, `fov_deg`	rectilinear, with this horizontal and vertical field of view of the full pane in degrees, each set on its own, so a pixel need not span the same angle across and down
`pitch_deg`	the view's pitch in degrees; negative looks down
`pane`	width and height of the full rectilinear view in pixels
`crop`	x, y, width and height of the part of the pane that is kept
`size`	width and height of `exo.mp4`

meta.json

manifest.jsonl holds the same rows, one per episode.

Field	Meaning
`episode_id`	`<recording_id>__t<NN>`
`recording_id`	the recording the episode was cut from
`task_index`	the task's position among the tasks found in the recording, counting from 0
`task_count`	how many tasks were found in the recording. Not every task has an episode, so some task indexes are missing
`task`	what the operator does in this episode
`skill`	the task's main skill
`outcome`	`success`, `failure` or `partial`
`start_in_recording_s`, `end_in_recording_s`	where the episode starts and ends in its recording, in seconds
`seconds`	the episode's length
`min_wrist_translation_valid_share`	of the two wrists, the lower share of steps with a valid translation. It is at least 0.5 in every episode
`gripper_version`	`v3`, or `v2` for the 2 recordings (27 episodes) made with the older gripper, which has black slotted jaws and no white finger pads
`exo_camera_lowered`	true when the recording was made after the top camera was mounted lower on 2026-09-28. It is false for every episode here
`exo_camera_height_cm`	height of the top camera's lens above the table in cm, 100 for every episode here
`exo_view`	the top camera's view, described under Cameras
`exo_camera_world`	`exo.mp4`'s camera in the recording's world frame (see actions.npz): `R`, a 3 x 3 matrix given as a list of rows, whose columns are the camera's x (right), y (down) and z (forward) axes in world coordinates; `t`, its position in m, which is (0, 0, 0) where the top camera stood for most of the recording and otherwise where it stood during that episode; and `K`, the fx, fy, cx, cy of `exo.mp4` as a pinhole, with pixel centres at integer coordinates. A world point X is at camera coordinates Rᵀ(X − t). `null` for a recording that has no world frame, and for an episode during which the top camera was moved

actions.npz

Actions are recovered from the wrist cameras. Each wrist camera is rigidly mounted to its gripper, so the camera's pose gives the gripper's pose, and each action is the pose change between consecutive frames. Poses come from visual SLAM, are scaled to metric units with the gripper's IMU, and are checked against the IMU on every frame. Every pose, action and opening value has a validity flag, and a value whose flag is false is NaN, so it cannot be used by accident.

N is the number of frames in the episode.

Key	Shape	Meaning
`t`	(N,)	time in seconds from the start of the episode, at 30 fps
`state`	(N, 14)	left wrist then right wrist, 7 values each: gripper tip position xyz (m), rotation vector (rad), gripper opening (0 closed, 1 fully open)
`state_valid`	(N, 14)	whether each value of `state` is valid
`left_action_local`, `right_action_local`	(N-1, 7)	the step from frame k to k+1: translation (m) and rotation vector (rad) in the wrist's own frame at k, then the change in opening
`left_action_valid`, `right_action_valid`	(N-1, 7)	whether each value of the action is valid
`left_chunk`, `right_chunk`	(N,)	the tracking chunk whose world frame the wrist's position is in (-1 in the rare frames no chunk covers)
`left_pose_source`, `right_pose_source`	(N,)	how the position was obtained (table below)
`left_rot_action_source`, `right_rot_action_source`	(N-1,)	0 rotation from visual SLAM, 1 from the gyroscope, 2 from a rotation interpolated across a gap where no tracked rotation exists, -1 invalid
`left_filled`, `right_filled`	(N,)	frames whose position was interpolated across a gap of up to 2 s (pose source 5)
`left_gravity_cam`, `right_gravity_cam`	(N, 3)	the unit vector of gravity (pointing down) in the wrist camera's frame (x right, y down, z forward)
`left_pose_world`, `right_pose_world`	(N, 6)	the gripper tip's pose in the recording's world frame: position xyz (m), then rotation vector (rad), NaN where it is not known

Where the states at k and k+1 are both valid and in the same chunk, the action is exactly the change in state. For rotation matrices R and positions p:

translation: R_kᵀ (p_{k+1} − p_k)
rotation: the rotation vector of R_kᵀ R_{k+1}
gripper: g_{k+1} − g_k

Each chunk has its own world frame, so positions from different chunks cannot be compared directly. The local actions are in the wrist's own frame, so they stay valid across a change of chunk. Each step takes its action from a chunk that tracked both of its ends, so an action can be valid where one of the two states is not.

A chunk's world frame is in general not aligned with gravity. The gravity arrays give the direction of gravity in each wrist camera's own frame at every frame, so a pose can be put in a gravity-aligned frame. In the chunk's world frame, gravity at frame k is R_k g_k, where R_k comes from the rotation in state and g_k is the row of the gravity array. Gravity comes from the wrist camera's accelerometer, averaged over the chunk in the chunk's world frame. A row is NaN where the wrist's rotation is not valid, and in chunks where gravity could not be checked, either because it disagrees with the accelerometer by more than 3 degrees while the wrist moves or because the chunk's tracking could not be reproduced.

Every recording also has one world frame that both wrists and the top camera share. Its origin is the top camera's lens where it stood for most of the recording, z points up (against gravity), x is horizontal and points the way the top camera was tilted there, close to the direction of the top of exo.mp4's image, and y completes a right-handed frame. In some recordings the top camera was knocked or turned; the world frame stays where it was, and each episode's exo_camera_world is the camera where it stood during that episode. left_pose_world and right_pose_world give each gripper tip's pose in it, so the two grippers can be compared directly, across chunks and across all the episodes of one recording. Within a chunk, the world pose is the pose in state moved by one rigid transform, so it changes exactly as state does, and the local actions hold in either. Positions in the world frame are accurate to about 3.5 cm for the position of one gripper relative to the other (median on a calibration recording, measured against markers seen by all three cameras). A gripper's absolute position can be off by a few percent of its distance from the top camera, about 2 to 4 cm. A row of left_pose_world or right_pose_world is NaN where that wrist's position or rotation in state is not valid, and for every frame of a chunk or a wrist that could not be placed in the world frame. Over both wrists, 87.1% of the frames with a valid position and rotation have a world pose. 4,405 of the 4,587 episodes have world poses, and 4,364 have exo_camera_world. exo_camera_world in meta.json places exo.mp4's camera in the same frame. Because exo.mp4 is a flat view rendered from a fisheye lens, its pinhole K places a point a few pixels from where exo.mp4 itself shows it (the camera model alone, apart from the accuracy of the poses above): about 4 px median on the 696 x 448 videos, 4 px median on the 746 x 480 videos.

Pose source	Meaning
0	tracked by visual SLAM
1	joined from a second SLAM track of the same stretch
2	bridged with the IMU across a short gap
4	bridged with the IMU across a longer gap, and confirmed by the top camera
5	interpolated across a gap of up to 2 s
-1	no valid position (the rotation can still be valid)

Code 3 is not used.

Share of valid steps, over both wrists and every episode:


Translation	96.2%
Rotation	100.00%
Gripper opening	98.5%
Both wrists translated in the same step	92.9%

The gripper opening is measured from the wrist camera, from fully closed (0) to fully open (1). Operators rarely open the jaws all the way: the median episode's widest opening is 0.70, and 18% of episodes reach 0.95.

Labels

The labels of every episode are in their own dataset, DiverseUMI-sample25-annotations, one line per episode with its episode_id. Its card describes every field.

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 an episode

import json, numpy as np
from huggingface_hub import hf_hub_download

ep_id = "<episode_id>"
ep = f"freeform/{ep_id}"
A = np.load(f"{ep}/actions.npz")
meta = json.load(open(f"{ep}/meta.json"))
labels = next(r["labels"] for r in map(json.loads, open(hf_hub_download("Pantheon-inc/DiverseUMI-sample25-annotations", "labels.jsonl", repo_type="dataset")))
              if r["episode_id"] == ep_id)

ok = A["left_action_valid"][:, :3].all(1)
left_translation = A["left_action_local"][ok, :3]     # m per frame, in the left wrist's frame
print(meta["task"], meta["skill"], labels["completion"]["outcome"])
for step in labels["timeline"][:10]:
    print(step["start_s"], step["end_s"], step["arm"], step["action"], step["progress"])

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