Datasets:

challenge-2026
/

challenge_data

This directory is a standard LeRobot v2.1 dataset containing 5 bimanual manipulation episodes, together with left and right wrist-mounted ego-camera data.

pip install "lerobot==0.3.3"

from lerobot.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset(
    repo_id="local/umi_sample_data_v21",
    root="/path/to/umi_sample_data_v21",
)

Overview

Episode	`task_index`	Task	Frames	Duration
`episode_000000`	0	fold the red shirt	1410	47 s
`episode_000001`	1	fold the black shirt	1050	35 s
`episode_000002`	2	fold the yellow shirt	870	29 s
`episode_000003`	1	fold the black shirt	1050	35 s
`episode_000004`	3	fold the brown shirt	1560	52 s

The dataset has 5940 frames and 4 unique tasks, at 30 FPS, with 960 × 960 ego video.

Directory structure

umi_sample_data_v21/
├── data/chunk-000/                       # 5 episode Parquet files
├── videos/chunk-000/
│   ├── observation.images.left_ego/      # left-hand ego video
│   └── observation.images.right_ego/     # right-hand ego video
├── meta/
│   ├── info.json                         # dataset and field definitions
│   ├── tasks.jsonl                       # task to task_index mapping
│   ├── episodes.jsonl                    # episode lengths and tasks
│   ├── episodes_stats.jsonl              # per-episode statistics
│   └── calibration.json                  # camera and IMU calibration
├── annotation/                           # episode-level task and action-segment annotation
└── imu/                                  # left and right IMU data

Field reference

Images

Dataset field	Source
`observation.images.left_ego`	Left wrist-mounted RGB ego camera, 960 × 960
`observation.images.right_ego`	Right wrist-mounted RGB ego camera, 960 × 960

Proprioception and actions

observation.state and action are both 16-dimensional, in the same field order:

Indices	Field order	Meaning	Unit
0–2	`left_x, left_y, left_z`	Left end-effector position	m
3–6	`left_qw, left_qx, left_qy, left_qz`	Left end-effector quaternion `(w, x, y, z)`	-
7	`left_gripper`	Left gripper opening angle	°
8–10	`right_x, right_y, right_z`	Right end-effector position	m
11–14	`right_qw, right_qx, right_qy, right_qz`	Right end-effector quaternion `(w, x, y, z)`	-
15	`right_gripper`	Right gripper opening angle	°

observation.state is the current-frame state. Except for the final frame, action[t] = state[t+1]; the final frame's action keeps the next target from the original capture sequence, so it is not necessarily equal to this episode's final state. Left and right poses live in independent coordinate frames — you cannot directly compute the relative distance or orientation between the two hands.

Index fields

Field	Type	Description
`timestamp`	float32	Time within the episode, in seconds
`frame_index`	int64	Frame number within the episode, 0-based
`episode_index`	int64	Episode number, range 0–4
`index`	int64	Global frame number in the dataset, range 0–5939
`task_index`	int64	Task number, keyed to `meta/tasks.jsonl`

Annotations and IMU

File	Description
`annotation/episode_subtasks_*.jsonl`	Episode-level task, target objects and success flag
`annotation/action_steps_*.jsonl`	Fine-grained action segments
`imu/episode_*_{left,right}.csv`	Left and right timestamps, three-axis angular velocity and acceleration

Annotation intervals use the half-open convention [start_frame_index, end_frame_index) — the start frame is included, the end frame is not.

The subtask_index field of action_steps is constantly 0 across this delivery. It is a placeholder — do not use it as a foreign key into episode_subtasks; derive the mapping between the two layers from frame-interval overlap instead. This dataset carries exactly one episode_subtasks row per episode (= the whole-episode task).