eval_pi05_candy_sorting_ood (TsFile)

Apache TsFile version of allenai/eval_pi05_candy_sorting_ood.

Overview

A LeRobot v3.0 manipulation dataset recorded with a bi_yam_follower robot arm, converted to a single Apache TsFile table.

• Scale: 24 episodes / 15,757 frames at 30 fps (~9 min of robot time) / 4 task(s).

• Per-frame vectors: observation.state and action carry 14 joint values per frame (flattened to FLOAT columns here).

• Task language: Sort Candy; Place The Candies Into The Boxes From Left To Right In The Order of Orange, Blue, And Gold; Keep The Candies Into The Boxes By Type; Store The Candies Separately Into The Boxes

• Cameras (not included here): observation.images.right, observation.images.left, observation.images.top.

• Tables: 1 TsFile table(s), 15,757 rows in total.

  • eval_pi05_candy_sorting_ood: 15,757 rows, Time restarts inside every episode.

Schema (TsFile structure)

eval_pi05_candy_sorting_ood.tsfile

• Time (INT64, milliseconds) — round(timestamp × 1000); the source timestamp column is folded into Time.
• TAG: episode_index, task_index — device dimension; query one device with WHERE episode_index=....
• FIELD (30): frame_index (int64), sample_index (int64), action_0 (float), action_1 (float), action_2 (float), action_3 (float), action_4 (float), action_5 (float), action_6 (float), action_7 (float), action_8 (float), action_9 (float), action_10 (float), action_11 (float), action_12 (float), action_13 (float), observation_state_0 (float), observation_state_1 (float), observation_state_2 (float), observation_state_3 (float), observation_state_4 (float), observation_state_5 (float), observation_state_6 (float), observation_state_7 (float), observation_state_8 (float), observation_state_9 (float), observation_state_10 (float), observation_state_11 (float), observation_state_12 (float), observation_state_13 (float)

Conversion notes

• LeRobot v3.0 robot dataset converted with the project's generic lerobot converter: all episodes of the source train split share one TsFile, with the source columns episode_index and task_index as TAG columns (the TsFile device dimension). Query a single episode with WHERE episode_index=0.
• Vector columns preserve the full source column name when flattened: . becomes _ and the element index is appended, so observation.state becomes observation_state_0..N-1 and action becomes action_0..N-1, all single-precision FLOAT. frame_index is kept and the source index column is kept as sample_index.
• Time = round(timestamp * 1000) ms and restarts inside each episode. The source timestamp column is not stored separately because it equals Time / 1000 seconds.
• Camera video streams are NOT included in this repository; see the original dataset for them: https://huggingface.co/datasets/allenai/eval_pi05_candy_sorting_ood/tree/main/videos
• meta/ is mirrored from the source with meta/info.json updated to describe the converted TsFile artifact. No numeric time-series row is dropped.
• The source Parquet files carry a pandas schema-metadata block whose recorded numpy_type is fixed_size_list<element: float>[14][pyarrow], which is not a valid pandas dtype; that metadata hint was removed before reading so the values could be decoded. Column types and values are unchanged.

Usage

Read this dataset with the Java SDK. It is written with the zstd compressor. The Apache TsFile Java SDK reads every compressor, but the Python SDK (pip install tsfile) implements only UNCOMPRESSED, SNAPPY, GZIP and LZ4; opening this file with it yields no rows.

Read a converted file with the Apache TsFile Java SDK (org.apache.tsfile:tsfile):

import org.apache.tsfile.file.metadata.IDeviceID;
import org.apache.tsfile.read.TsFileReader;
import org.apache.tsfile.read.TsFileSequenceReader;
import org.apache.tsfile.read.common.Path;
import org.apache.tsfile.read.expression.QueryExpression;
import org.apache.tsfile.read.query.dataset.QueryDataSet;
import org.apache.tsfile.write.schema.MeasurementSchema;

import java.util.ArrayList;
import java.util.List;

String path = "data/eval_pi05_candy_sorting_ood.tsfile";
try (TsFileSequenceReader sequenceReader = new TsFileSequenceReader(path);
     TsFileReader reader = new TsFileReader(sequenceReader)) {
    // Devices are enumerated as IDeviceID objects. Tables with TAG columns
    // have multi-segment device ids that cannot be rebuilt from their string
    // form, so always use getAllDevices() rather than getAllDeviceIds().
    for (IDeviceID device : sequenceReader.getAllDevices()) {
        List<Path> paths = new ArrayList<>();
        for (MeasurementSchema schema : reader.getMeasurement(device)) {
            String name = schema.getMeasurementName();
            if (name != null && !name.isEmpty() && !name.equalsIgnoreCase("Time")) {
                paths.add(new Path(device, name, true));
            }
        }
        QueryDataSet dataSet = reader.query(QueryExpression.create(paths, null));
        while (dataSet.hasNext()) {
            System.out.println(dataSet.next());
        }
    }
}

Source & license

• Original dataset: allenai/eval_pi05_candy_sorting_ood
• License: apache-2.0 (as declared by the original dataset).