Datasets:

Shiki42
/

PutCab-AB-RandomDelta-Train50

---
pretty_name: PutCab-AB-RandomDelta-Train50
library_name: lerobot
task_categories:
- robotics
language:
- en
tags:
- lerobot
- robotwin
- balanced-retiming
configs:
- config_name: default
  data_files:
  - split: train
    path: data/chunk-000/*.parquet
---
# PutCab-AB-RandomDelta-Train50

**50 qualified episodes, 11,614 frames**, with three 640×480 RGB cameras at 50/3 FPS. This revision replaces the earlier prefix-selected Train50 with a timing-balanced selection from the **current frozen pool of 147 qualified trajectories** (117 reusable v3 and 30 completed new V2). The 200-trajectory generation remains ongoing; this release is a subset of the frozen current pool, rather than a completed 200-episode parent release.

## Selection

Normalized reference offset is `u_ref=(delta+T_B_ref)/(T_A_ref+T_B_ref)`, where delta is right/B start minus left/A start. Reference durations trim exact endpoint holds and exclude added waits. Ten intervals [0,.1), …, [.9,1] each contribute **five** trajectories. Bins 0–4 select negative deltas; bins 5–9 select positive deltas, giving **25 negative and 25 positive** episodes. Selection is without replacement within each eligible bin/sign stratum, followed by shuffling, using NumPy PCG64 seed **2026090905**. It is deliberately conditioned on bin and sign, and is distinct from a uniform random sample of the entire source pool. The recorded bin balance applies to normalized reference offset; raw seconds across Seed durations form a mixture.

![Delta distribution](meta/plots/balanced_delta_distribution.png)

[Per-episode offsets](meta/episode_delta.csv) and [distribution](meta/delta_distribution.json) record the measured selection. `meta/selection.json` preserves eligible stratum sizes, snapshot hash, pool indices and stable source-plus-candidate parent IDs. `meta/pool-snapshot.json` records the frozen qualified pool. All 50 original source Seeds remain eligible in generation; this selected set may cover fewer distinct Seeds.

## Expert behavior and evidence

Reused trajectories retain independent active-arm v3 timing. New V2 trajectories preserve arm paths and add the episode's recorded feedback waits: negative-delta motion lifts to a safe waypoint before approaching the cabinet and checks drawer readiness again before placement. Pose/path progression pauses during these waits while the other arm continues. Episode-specific `expert_policy`, `reference_stable_seconds`, `wait_seconds`, and `drawer_gate_events` are retained in episode metadata and lineage. Policy effects and timing balance should be considered separately when interpreting training results.

Every selected payload has verified task success and full-scene maximum penetration ≤10 mm inclusive, including robot, camera bodies and cabinet contacts. The saved hashed controls are replayed to render observations and recheck qualification. Both wrist cameras have 90° vertical FOV. All video frames, timestamps, camera calibration, path progression and payload hashes are validated. `meta/qualification` retains rendering, validation and retiming receipts.

`observation.state` contains 14 joint/gripper drive targets; `action` is the next observation target with the terminal target repeated. Physics runs at 250 Hz and observations every 15 ticks give 50/3 FPS. Episode/global/frame IDs are contiguous and all rows use the train split. Per-episode and full selected-set numeric/image statistics are included. Source instructions remain unchanged.

## Provenance

Input trajectories: `Shiki42/robotwin_putcab_fixed_leftDrawer_rightObject_AthenB_50` at `793556b0d670af13653dcb6a4ba99864fd985c59`. Raw Seed/scene mapping: `Shiki42/robotwin_putcab_fixed_role_source75` at `b5ffd87a13cde9a376caca08245b6f202319cb00`. RoboTwin base `59ccd4a347396bf30bf324a61bba9b4cdf63a2d6`; immutable runtime `parallelvla-2026-08-22.2`. Source/policy/script identities are recorded in provenance and qualification files.

RoboTwin code carries MIT, copyright 2025 Tianxing Chen and contributors. A blanket license for upstream data and rendered third-party assets remains unspecified. SHA256SUMS binds this card and all artifact files; the local content identity is explicit in `.ctr-revision.json`, while the external publishing receipt records the verified immutable public revision. Evidence lifecycle: reported / audit pending. Training and policy evaluation remain separate experiments.

	---
	pretty_name: PutCab-AB-RandomDelta-Train50
	library_name: lerobot
	task_categories:
	- robotics
	language:
	- en
	tags:
	- lerobot
	- robotwin
	- balanced-retiming
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/chunk-000/*.parquet
	---

	# PutCab-AB-RandomDelta-Train50

	50 qualified episodes, 11,614 frames, with three 640×480 RGB cameras at 50/3 FPS. This revision replaces the earlier prefix-selected Train50 with a timing-balanced selection from the current frozen pool of 147 qualified trajectories (117 reusable v3 and 30 completed new V2). The 200-trajectory generation remains ongoing; this release is a subset of the frozen current pool, rather than a completed 200-episode parent release.

	## Selection

	Normalized reference offset is `u_ref=(delta+T_B_ref)/(T_A_ref+T_B_ref)`, where delta is right/B start minus left/A start. Reference durations trim exact endpoint holds and exclude added waits. Ten intervals [0,.1), …, [.9,1] each contribute five trajectories. Bins 0–4 select negative deltas; bins 5–9 select positive deltas, giving 25 negative and 25 positive episodes. Selection is without replacement within each eligible bin/sign stratum, followed by shuffling, using NumPy PCG64 seed 2026090905. It is deliberately conditioned on bin and sign, and is distinct from a uniform random sample of the entire source pool. The recorded bin balance applies to normalized reference offset; raw seconds across Seed durations form a mixture.

	![Delta distribution](meta/plots/balanced_delta_distribution.png)

	[Per-episode offsets](meta/episode_delta.csv) and [distribution](meta/delta_distribution.json) record the measured selection. `meta/selection.json` preserves eligible stratum sizes, snapshot hash, pool indices and stable source-plus-candidate parent IDs. `meta/pool-snapshot.json` records the frozen qualified pool. All 50 original source Seeds remain eligible in generation; this selected set may cover fewer distinct Seeds.

	## Expert behavior and evidence

	Reused trajectories retain independent active-arm v3 timing. New V2 trajectories preserve arm paths and add the episode's recorded feedback waits: negative-delta motion lifts to a safe waypoint before approaching the cabinet and checks drawer readiness again before placement. Pose/path progression pauses during these waits while the other arm continues. Episode-specific `expert_policy`, `reference_stable_seconds`, `wait_seconds`, and `drawer_gate_events` are retained in episode metadata and lineage. Policy effects and timing balance should be considered separately when interpreting training results.

	Every selected payload has verified task success and full-scene maximum penetration ≤10 mm inclusive, including robot, camera bodies and cabinet contacts. The saved hashed controls are replayed to render observations and recheck qualification. Both wrist cameras have 90° vertical FOV. All video frames, timestamps, camera calibration, path progression and payload hashes are validated. `meta/qualification` retains rendering, validation and retiming receipts.

	`observation.state` contains 14 joint/gripper drive targets; `action` is the next observation target with the terminal target repeated. Physics runs at 250 Hz and observations every 15 ticks give 50/3 FPS. Episode/global/frame IDs are contiguous and all rows use the train split. Per-episode and full selected-set numeric/image statistics are included. Source instructions remain unchanged.

	## Provenance

	Input trajectories: `Shiki42/robotwin_putcab_fixed_leftDrawer_rightObject_AthenB_50` at `793556b0d670af13653dcb6a4ba99864fd985c59`. Raw Seed/scene mapping: `Shiki42/robotwin_putcab_fixed_role_source75` at `b5ffd87a13cde9a376caca08245b6f202319cb00`. RoboTwin base `59ccd4a347396bf30bf324a61bba9b4cdf63a2d6`; immutable runtime `parallelvla-2026-08-22.2`. Source/policy/script identities are recorded in provenance and qualification files.

	RoboTwin code carries MIT, copyright 2025 Tianxing Chen and contributors. A blanket license for upstream data and rendered third-party assets remains unspecified. SHA256SUMS binds this card and all artifact files; the local content identity is explicit in `.ctr-revision.json`, while the external publishing receipt records the verified immutable public revision. Evidence lifecycle: reported / audit pending. Training and policy evaluation remain separate experiments.