azithromycin/LSY-lab_libero_plus_goal_camera_all_ur5e_failures

LIBERO Plus Goal Camera All UR5e Failures TsFile

This repository contains an Apache TsFile conversion of LSY-lab/libero_plus_goal_camera_all_ur5e_failures, a LeRobot v3.0 UR5e manipulation dataset with successful and failed trajectories.

Source and Attribution

• Publishing organization: LSY-lab / Learning Systems Lab
• Original uploader and sole contributor shown in the source file history: Maximilian Christof (max-chr)
• License: Apache-2.0
• Source split: train (episodes 0:280)
• Source layout: one Parquet shard at data/chunk-000/file-000.parquet
• Source size: 10,870,000 bytes, 61,618 rows
• Robot: UR5e
• Sampling rate: 20 fps
• Episodes: 280
• Tasks: 6
• Paper/citation: the source dataset card does not provide a dataset-specific paper or BibTeX citation.

Task indexes are: 0 put the wine bottle on the rack; 1 open the middle layer of the drawer; 2 put the bowl on the top of the drawer; 3 open the top layer of the drawer and put the bowl inside; 4 push the plate to the front of the stove; and 5 put the cream cheese on the bowl.

Converted Artifact

• TsFile: data/lsy_lab_libero_plus_goal_camera_all_ur5e_failures.tsfile
• Table: lsy_lab_libero_plus_goal_camera_all_ur5e_failures
• Files/shards: 1
• Rows: 61,618
• TAG devices: 280, one for each distinct (episode_index, task_index)
• Time precision: milliseconds
• TsFile size: 7,260,077 bytes (66.79% of the 10,870,000-byte source frame Parquet)

Schema

TsFile columns	Type	Role	Source
Time	TIMESTAMP/INT64	TIME	round(timestamp * 1000) ms
episode_index, task_index	STRING	TAG	Source indexes represented by the TsFile device/tag mechanism
frame_index, sample_index	INT64	FIELD	frame_index; index renamed to sample_index
observation_state_0..7	FLOAT	FIELD	observation.state[8]
observation_states_ee_state_0..5	FLOAT	FIELD	observation.states.ee_state[6]
observation_states_joint_state_0..5	FLOAT	FIELD	observation.states.joint_state[6]
observation_states_gripper_state_0..1	FLOAT	FIELD	observation.states.gripper_state[2]
action_0..6	FLOAT	FIELD	action[7]

Vector columns are flattened in row-major order. Source periods are replaced by underscores and the element index is appended. Rows are sorted by TAG columns and then Time; Time restarts for each episode.

The source timestamp field is dropped only after producing Time because it is redundant with Time / 1000 seconds. The source index field is retained as sample_index. No rows or other numeric time-series fields are dropped.

Encoding and Compression

Physical type	Encoding	Compression
FLOAT / DOUBLE	GORILLA	LZ4
INT32 / INT64	TS_2DIFF	LZ4
Time	TS_2DIFF	LZ4
BOOLEAN	RLE	LZ4
TAG	TsFile table device/tag storage	TsFile-managed

The source frame schema contains no BOOLEAN FIELD, so the BOOLEAN mapping is documented but not exercised by this artifact.

Videos

Videos are intentionally not included in this TsFile repository. The original 706 MB video tree remains at the source videos/ directory. It contains 280 episode files for each of four streams:

• videos/observation.images.image/chunk-000/file-*.mp4
• videos/observation.images.wrist_image/chunk-000/file-*.mp4
• videos/observation.images.frontview_image/chunk-000/file-*.mp4
• videos/observation.images.sideview_image/chunk-000/file-*.mp4

The preserved meta/episodes/chunk-000/file-000.parquet records per-episode video chunk/file indexes and timestamp ranges for frame alignment.

Validation

The TsFile was reopened with the Apache TsFile Python SDK. Its table schema contains 34 columns; authoritative TsFile metadata and full query readback both returned 61,618 rows across 280 TAG devices, matching the staged Parquet exactly. Time is monotonic within each (episode_index, task_index) group and Time == round(source_timestamp * 1000).

Usage

from tsfile import ColumnCategory, TsFileReader

path = "data/lsy_lab_libero_plus_goal_camera_all_ur5e_failures.tsfile"
reader = TsFileReader(path)
table_name = "lsy_lab_libero_plus_goal_camera_all_ur5e_failures"
schema = reader.get_all_table_schemas()[table_name]
columns = [
    c.get_column_name()
    for c in schema.get_columns()
    if c.get_category() in (ColumnCategory.FIELD, ColumnCategory.TAG)
]

with reader.query_table(table_name, columns, batch_size=65536) as result:
    batch = result.read_arrow_batch()