README.md

metadata

pretty_name: DROID SAM 3.1 Segmentation Masks
task_categories:
  - image-segmentation
tags:
  - robotics
  - rlds
  - droid
  - sam3
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*.parquet

DROID SAM 3.1 Segmentation Masks

This dataset is a mask-only sidecar generated from the original droid_101/0.0.1 RLDS release. It does not redistribute DROID images or actions. Its episode_index follows the RLDS episode order.

The same episodes appear in lerobot/droid_1.0.1, but LeRobot stores them in a different episode order. Therefore, mask episode_index and LeRobot episode_index must not be joined directly. Use the mapping file described below to associate these masks with LeRobot episodes.

Prompts

• exterior_image_1_left: robot manipulator
• exterior_image_2_left: robot manipulator
• wrist_image_left: end-effector

Masks are per-step semantic unions encoded as compressed COCO RLE dictionaries with size and ASCII counts. Episodes were processed in independent chunks of 800 frames (0 means a full episode).

Source TFDS subsplits: 201

SAM source revision: 46957e47805eaa273f4aa7bbbd25a88bca9108ce

Weight set: gripper_universal

SAM checkpoint revision: daa63191845a41281374e725f4c9e51c7a824460

Gripper detector repository: sazirarrwth99/sam3.1-gripper-universal

Gripper detector revision: be4e08e989a673511b11b7214fb135639dc5132a

LeRobot to RLDS episode mapping

lerobot_to_rlds_episode_mapping.parquet provides the episode correspondence between LeRobot and RLDS. The mapping contains 95,658 rows, one per LeRobot hf_episode_index. The RLDS shard, record, and source identity are included for additional verification.

Important: rlds_shard_major_index and this dataset's episode_index column are both positional counters, not stable identifiers — do not use either to index directly into data/*.parquet. Always join on episode_id (this dataset's column) against rlds_episode_id (the mapping's column), which is content-addressed and stable across row-order changes.

LeRobot-side fields include hf_episode_index, hf_length, hf_data_path, hf_dataset_from_index, and hf_dataset_to_index. RLDS-side fields include rlds_shard_index, rlds_record_index, rlds_shard_major_index, rlds_tfrecord_path, rlds_byte_offset, rlds_file_path, rlds_recording_folderpath, and rlds_episode_id.

All 95,658 episodes were mapped with zero unmatched or unresolved episodes. Matches were verified using exact equality of the complete float32 action.joint_position sequence. Of these, 95,466 mappings are unique. The remaining 192 rows form 96 exact duplicate-equivalence groups: the RLDS source contains indistinguishable copies of the same recording under aliased source identities. For these rows, the physical record is paired deterministically; copy-level provenance cannot be recovered from identical episode content. Check mapping_uniqueness, rlds_candidate_count, and the candidate_rlds_* columns when this distinction matters.

Example lookup in either direction:

import pyarrow.compute as pc
import pyarrow.parquet as pq
from datasets import load_dataset

mapping = pq.read_table("lerobot_to_rlds_episode_mapping.parquet")
masks = load_dataset("EpicPinkPenguin/droid_dataset_segmentation_mask", split="train")
episode_id_to_row = {episode_id: row for row, episode_id in enumerate(masks["episode_id"])}

# LeRobot episode -> mask row
match = mapping.filter(pc.equal(mapping["hf_episode_index"], 12345))
rlds_episode_id = match["rlds_episode_id"][0].as_py()
mask_row = episode_id_to_row[rlds_episode_id]

# Mask row -> LeRobot episode
episode_id = masks[mask_row]["episode_id"]
match = mapping.filter(pc.equal(mapping["rlds_episode_id"], episode_id))
lerobot_episode_index = match["hf_episode_index"][0].as_py()

SHA-256: 7548f287fb4f2d62c642e4e89a8b7a5ac50d278d18b1d2ff0b042e4c7309eb5a

Generated with the DROID segmentation annotation pipeline. The original DROID dataset and SAM checkpoint remain subject to their own licenses and terms.