Ronaldo-GOAT/text_audio_pretraining_for_GAN

bw_jh_dataset — Robot 3D Point-Track Dataset (droid / hrdexdb / robocasa)

A unified multi-source robot manipulation dataset with 3D point tracks of the robot gripper and arm, calibrated multi-view RGB video, language instructions, and success labels. Three splits:

Split	Episodes	Size	Views	Source
droid	27,615 (+~1,050 in droid-11)	~280 GB	2 exterior ZED views	DROID (real, Franka)
hrdexdb	1,601	~998 GB	22 calibrated cameras	HRDexDB (real, xArm6 + dexterous hands)
robocasa	40,026	~273 GB	2 agentviews + ego wrist cam	RoboCasa (sim, Panda)

Getting the data

Each split ships as tar shards. Files larger than HF's 50 GB limit are binary-split into *.tar.partNN pieces — reassemble with:

cat hrdexdb-4.tar.part* > hrdexdb-4.tar   # parts are in order: part01, part02, ...
tar -xf hrdexdb-4.tar

Unsplit shards extract directly: tar -xf droid-01.tar. Every shard has a *.txt / *.manifest.txt file listing its episode ids.

Sample format (identical across splits)

Each episode is one directory named by its id:

<episode_id>/
  view1.mp4, view2.mp4, ...   # RGB videos (hrdexdb: up to view23; robocasa: also ego.mp4)
  gripper.npz                 # points: (T, 256, 3) float32 — 3D gripper point tracks
  robot.npz                   # points: (T, 512, 3) float32 — 3D robot arm point tracks
  meta.json                   # calibration + labels, see below

• All 3D points are in the view1 camera frame (OpenCV convention, meters).
• meta.json fields: views (per-view T_cam_world 4×4 with extrinsics_convention: "cam_from_world", and intrinsics {fx, fy, cx, cy, k1, k2, p1, p2, k3, width, height}), language_instructions, language_source, success, success_source, fps, n_frames, task_category, uuid.
• To project points into view v at frame t: p_world = inv(T_view1) @ p_v1, then p_v = T_v @ p_world, then apply view v's intrinsics (+ distortion). robocasa's ego view has per-frame extrinsics (T, 4, 4).

DROID split contents

The droid shards here (droid-XX-success-lang.tar) contain only success episodes with language instructions (27,615 episodes = success ∩ language-annotated, drawn from our calibration-filtered export list), and their meta.json files are already patched with the verified instructions (merged from the official aggregated annotations and the KarlP/droid 75k release) — no post-processing needed. Each shard has a matching droid-XX-success-lang.txt episode-id list. droid-11-success-lang.tar adds the same filtering over episodes exported after the main shards.

For reference/audit, droid/droid_language_recovered.json documents the full language-recovery provenance (per-entry source, donor, n_donors, donor_agreement) — see droid_language_recovered_README.md. droid-meta-patch-success.tar is the standalone metadata patch used to produce these shards.

Visualizer

tools/visualize_episode.py renders a row-concatenated multi-view video with the 3D point tracks re-projected onto every view using only the stored calibration — use it to verify calibration/npz consistency:

python tools/visualize_episode.py --split droid --episode 0 --root <extracted_root>
python tools/visualize_episode.py --split hrdexdb --episode allegro_v5__apple__3 --root <extracted_root>

--episode accepts an integer index or an episode id. Dependencies: numpy, opencv-python. Example outputs are in tools/examples/.

Verification pairs

droid/debug_lang_pairs/ contains side-by-side fail-vs-success videos demonstrating the failure-instruction borrowing (failure left/red, same-session success donor right/green) with pair_info.json provenance for each.

Provenance & licenses

• droid: derived from DROID (CC-BY-4.0); language annotations merged from the official aggregated annotations and the KarlP/droid 75k release.
• robocasa: derived from RoboCasa datasets.
• hrdexdb: HRDexDB multi-camera teleoperation recordings.
• Success labels come from the source datasets' own labels/path conventions; 8 label-contradictory droid episodes were excluded entirely.