Release v3: 124800 demonstrations across 24 goal orientations

Preserves all 62400 v2 episodes and adds 62400 verified demonstrations at 15,45,...,345 degrees. 52 letters x 24 orientations x 100 episodes. CC BY 4.0.

CITATION.bib

@@ -11,6 +11,6 @@
   title = {Push Any Letter: Center-12 High-Friction Demonstrations},
   year = {2026},
   howpublished = {Hugging Face},
-  note = {Version center12-friction14-v2, CC BY 4.0},
+  note = {Version center24-friction14-v3, CC BY 4.0},
   url = {https://huggingface.co/datasets/DavidLXu/Push-Any-Letter}
 }

README.md

@@ -7,60 +7,41 @@ tags:
 - imitation-learning
 - reinforcement-learning
 - world-models
-- pushing
 size_categories:
-- 10K<n<100K
+- 100K<n<1M
 ---
 
-# Push Any Letter — Dataset v2
+# Push Any Letter — Dataset v3
 
-**62,400 replay-validated successful planner demonstrations:** 52 uppercase/lowercase ASCII letters × 12 center-goal orientations × 100 trajectories per condition.
+**124,800 replay-validated successful demonstrations: 52 letters × 24 center-goal orientations × 100 trajectories.**
 
-[Project page](https://davidlxu.github.io/Push-Any-Letter/) · [Code and training protocol](https://github.com/DavidLXu/Push-Any-Letter) · [Original v1 (6,240 episodes)](https://huggingface.co/datasets/DavidLXu/Push-Any-Letter/tree/v1)
+V3 retains all 62,400 v2 episodes and adds 62,400 new episodes at 15°, 45°, …, 345°. The full goal-angle grid now has 15° spacing. Physics, font geometry, state/image/action schema, planner settings and success threshold are unchanged. All newly accepted trajectories are independently action-replayed and checked for duplicates. Added generation uses seed base 2,000,000,000.
 
-## What changed
+[Project page](https://davidlxu.github.io/Push-Any-Letter/) · [Code](https://github.com/DavidLXu/Push-Any-Letter) · [V2](https://huggingface.co/datasets/DavidLXu/Push-Any-Letter/tree/v2) · [V1](https://huggingface.co/datasets/DavidLXu/Push-Any-Letter/tree/v1)
 
-Slots 00–09 retain the original v1 trajectories and recorded arrays. Slots 10–99 add 56,160 new demonstrations with independent starting states and planner seeds. Release metadata is updated for v2. Geometry, centered targets, 30° goal-angle spacing, planner settings and physics are unchanged. New generation uses seed base 1,000,000,000. All 624 conditions contain 100 validated successes.
+## Files and provenance
 
-V1 remains available at tag `v1`, revision `8c02168906f991bc07ee3648bee6290babf13e47`. Use tag `v2` for this release. Previously trained v1 policies and evaluation results still refer to the 10-demonstration training set; expanding this dataset does not retroactively change those experiments.
+Original v2 shards are unchanged in `data/`; new-angle shards are in `data/interleaved/`. There are 104 shards total. `episodes.jsonl` describes all 124,800 episodes and their SHA-256 hashes; `SHA256SUMS` lists all shard and manifest hashes. `statistics.json` records measured counts, transitions, coverage, replay errors and the immutable parent-v2 revision. Inherited episodes retain their original release metadata.
 
-## Contents
+Use the repository downloader with `--revision v3` for this release; use `--revision v1` or `v2` to reproduce previous experiments. The existing downloader follows the manifest's shard paths. The expanded archives and extracted dataset require correspondingly more disk space.
 
-52 per-letter TAR shards in `data/`, with compressed NPZ episodes at `U0041/angle000/traj00.npz` through `traj99.npz`. `episodes.jsonl` lists reset poses, seed, steps, coverage, member paths and individual SHA-256 hashes. `SHA256SUMS` contains shard and manifest hashes. `statistics.json` gives full measured statistics.
+## Evaluation scope
 
-- Episodes: 62,400; control transitions: 47,172,050.
-- Steps per episode: 19–2398; mean 756.0.
-- Minimum final geometric coverage: 0.950000.
-- Maximum action-replay state error: 0.
-- State: `[agent_x, agent_y, object_x, object_y, object_angle, goal_x, goal_y, goal_angle]`.
-- Actions: absolute pusher targets `[x, y]` in pixels; bounds `[28, 612]²`.
-- RGB observations: pre-action `uint8 [N,96,96,3]`; control at 20 Hz with ten physics substeps.
-- Coordinates: top-left origin, positive y down; angles are radians, clockwise on screen.
+The inserted angles are now training data, so they cannot also serve as unseen-angle tests for a model trained on the full v3 set. Define fresh held-out angles or explicit train/test angle splits before reporting generalization. V1/V2 policy and world-model experiments remain tied to their original data versions; this release makes no claim of improved learned-model performance. Goals remain centered, with one font and one physics profile.
 
-Block friction 1.4, pusher friction 0.6, damping 0.12. Every accepted demonstration is independently replay-validated from its recorded reset state and actions. The generator checks for duplicate trajectories and this release checks inherited v1 identities.
+## Schema and attribution
 
-## Download and use
+State: `[agent_x, agent_y, object_x, object_y, object_angle, goal_x, goal_y, goal_angle]`; action: absolute pusher target in pixels. RGB is pre-action uint8 96×96×3. Control is 20 Hz, physics 200 Hz. Positions use a top-left origin and downward-positive y; angles are radians, clockwise on screen. Block friction 1.4, pusher friction 0.6, damping 0.12.
 
-```bash
-python examples/download_dataset.py --output datasets/push-any-letter-v2 --revision v2
-```
+Data is success-filtered planner demonstration data, not optimal trajectories or a representative failure distribution. Exact physics replay requires the matching Arial Bold font; font binaries are not distributed. Code is MIT, data CC BY 4.0; third-party font rights are separate. Created by Lixin Xu (许立昕); cite CITATION.bib and specify version v3. See the code repository for Push-T / Diffusion Policy attribution.
 
-The command is provided in the linked GitHub repository. Reserve space for both the approximately 10× larger archive set and extracted episodes. The same NPZ schema and downloader work for both versions; explicitly pin `--revision v1` to reproduce the original experiments.
+Measured transitions: 94,365,251. Minimum final coverage: 0.950000. Maximum replay error: 0. Parent v2 revision: `422f2eaadc6c76bf6451706da1d046b50ea1e9d8`.
 
-Exact physics replay requires the matching Arial Bold font, SHA-256 `d72db21f9242aedd6b917d8549ad5921766b24d5f8d0becfda2ff4c620b3c2e0`. Font binaries are not redistributed; use a legitimately available matching font. Recorded arrays can be read without the font.
-
-## Intended use and limitations
-
-Study imitation learning, online reinforcement learning and learned-dynamics/world-model evaluation across letter shapes and goal orientations. This is success-filtered planner data, not human demonstrations, optimal paths or a representative sample of failures. Goals remain centered; arbitrary goal-position, unseen-font and held-out-letter generalization require additional data or separate evaluation protocols. No v2 learned-policy result is claimed.
+## V1 policy evaluation report
 
-## Attribution
+The [completed bilingual technical report](https://davidlxu.github.io/posts/2026/10/push-any-letter-imitation-learning/) covers four state policies and four image policies trained on **V1: ten demonstrations per condition**. It includes all loss histories, separate 52-case training/test success curves, the full 520-case unseen-angle tests, and replayed examples. State ACT reaches 319/520 (61.35%); image ACT and Flow reach 227/520 and 226/520, respectively, a one-case difference from one training seed. These are V1 results and do not evaluate the larger V2 or V3 datasets. Pin `v1` or revision `8c02168906f991bc07ee3648bee6290babf13e47` to reproduce the report.
 
-Created by Lixin Xu (许立昕). Dataset: CC BY 4.0; code: MIT. Cite `CITATION.bib` and specify dataset version v2. The environment is inspired by Push-T / Diffusion Policy; see the code repository for method attribution. Dataset licensing does not grant rights to third-party fonts.
 
-## Related resources
+## Related reports
 
 [MIT](https://github.com/DavidLXu/Push-Any-Letter/blob/main/LICENSE) · [Diffusion Policy](https://diffusion-policy.cs.columbia.edu/) · [Flow Matching](https://arxiv.org/abs/2210.02747) · [ACT](https://tonyzhaozh.github.io/aloha/)
-
-## V1 policy evaluation report
-
-The [completed bilingual technical report](https://davidlxu.github.io/posts/2026/10/push-any-letter-imitation-learning/) covers four state policies and four image policies trained on **V1: ten demonstrations per condition**. It includes all loss histories, separate 52-case training/test success curves, the full 520-case unseen-angle tests, and replayed examples. State ACT reaches 319/520 (61.35%); image ACT and Flow reach 227/520 and 226/520, respectively, a one-case difference from one training seed. These are V1 results and do not evaluate the larger V2 dataset currently on this branch. Pin `v1` or revision `8c02168906f991bc07ee3648bee6290babf13e47` to reproduce the report.

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