README.md

---
task_categories:
- robotics
tags:
- robotics
- action-selection
- search
- branch-and-rollback
configs:
- config_name: searches
  data_files:
  - split: train
    path: meta/searches/*__seed5*.parquet
  - split: test
    path: meta/searches/*__seed4*.parquet
- config_name: nodes
  data_files:
  - split: train
    path: data/nodes/**/*__seed5*.parquet
  - split: test
    path: data/nodes/**/*__seed4*.parquet
- config_name: steps
  data_files:
  - split: train
    path: data/steps/**/*__seed5*.parquet
  - split: test
    path: data/steps/**/*__seed4*.parquet
---

# sim-branching-data

**At a state: several action chunks proposed from it, and how each one actually ended.**
A branch the search dropped was cut off mid-episode, so it is resumed from its own snapshot
and carried to a finish — the action nobody executed still gets an answer to *would this
have worked*.

44 searches · 14 tasks · 2,165 nodes, each with its own
state and image.

This repo hosts the data. **What it means, how it was produced and how to use it live in
the code that wrote it:** [https://gitlab.com/mahgoobi/rewind](https://gitlab.com/mahgoobi/rewind) — see its README for the format, the
search strategies, and worked examples of loading a record.

## Splits

Both halves live here, told apart by the `split` column of `meta/searches` rather than by
path. The blocks are disjoint by construction, so a scorer trained on one can be measured
on the other without having seen it.

| split | seeds | in this repo |
|---|---|---|
| `train` | collection seeds (50000+) — fans to learn from | 21 searches |
| `test` | the benchmark's own evaluation seeds (40000+) — what a scorer is measured on | 23 searches |

```python
from rewind.record.hub import index
runs  = index("mahgoobi/sim-branching-data")
train = [r for r in runs if r["split"] == "train"]
test  = [r for r in runs if r["split"] == "test"]
```

## Tasks

One row per task: how many searches it contributed, which splits, which scene configs, the
seeds, and the **benchmark commit** whose code built those scenes.

That last column is not bookkeeping. The benchmark's success criteria change over time —
`put_milktea_on_shelf` gained an upright requirement, `put_milktea_next_to_laptop` a 15°
tolerance — so two runs of one task under different commits are scored by different rules
and should not be pooled. Runs of a task the change did not touch stay comparable.

| task | runs | splits | configs | seeds | benchmark commit |
|---|---|---|---|---|---|
| `drop_apple_in_bin_ks` | 6 | test, train | kitchens_clean, kitchens_d10, kitchens_d15 | 40003, 40005, 50000 | [RoboPRO @ `120b0e0`](https://github.com/EAI-RSM/RoboPRO/commit/120b0e0d1f16b295f00c4951000db59cc7b1eaba), [RoboPRO @ `64840ce`](https://github.com/EAI-RSM/RoboPRO/commit/64840ce8ef7e468764cc725ba83c7492cd56615f) |
| `move_pen_to_box` | 2 | test, train | study_clean | 40000, 50000 | [RoboPRO @ `64840ce`](https://github.com/EAI-RSM/RoboPRO/commit/64840ce8ef7e468764cc725ba83c7492cd56615f) |
| `move_seal_next_to_box` | 2 | test, train | study_clean | 40000, 50000 | [RoboPRO @ `64840ce`](https://github.com/EAI-RSM/RoboPRO/commit/64840ce8ef7e468764cc725ba83c7492cd56615f) |
| `move_seal_onto_table` | 2 | test, train | study_clean | 40000, 50000 | [RoboPRO @ `64840ce`](https://github.com/EAI-RSM/RoboPRO/commit/64840ce8ef7e468764cc725ba83c7492cd56615f) |
| `pick_apple_from_bowl_ks` | 2 | test, train | kitchens_clean | 40003, 50000 | [RoboPRO @ `64840ce`](https://github.com/EAI-RSM/RoboPRO/commit/64840ce8ef7e468764cc725ba83c7492cd56615f) |
| `pick_bottle_from_fridge` | 2 | test, train | kitchenl_clean | 40000, 50000 | [RoboPRO @ `64840ce`](https://github.com/EAI-RSM/RoboPRO/commit/64840ce8ef7e468764cc725ba83c7492cd56615f) |
| `pick_boxdrink_from_basket` | 2 | test, train | kitchenl_clean | 40000, 50000 | [RoboPRO @ `64840ce`](https://github.com/EAI-RSM/RoboPRO/commit/64840ce8ef7e468764cc725ba83c7492cd56615f) |
| `put_bottle_in_basket` | 2 | test, train | kitchenl_clean | 40001, 50000 | [RoboPRO @ `64840ce`](https://github.com/EAI-RSM/RoboPRO/commit/64840ce8ef7e468764cc725ba83c7492cd56615f) |
| `put_bottle_in_fridge` | 2 | test, train | kitchenl_clean | 40000, 50000 | [RoboPRO @ `64840ce`](https://github.com/EAI-RSM/RoboPRO/commit/64840ce8ef7e468764cc725ba83c7492cd56615f) |
| `put_bread_on_board_ks` | 2 | test, train | kitchens_clean | 40003, 50000 | [RoboPRO @ `64840ce`](https://github.com/EAI-RSM/RoboPRO/commit/64840ce8ef7e468764cc725ba83c7492cd56615f) |
| `put_milktea_next_to_laptop` | 7 | test, train | office_clean, office_d10, office_d15, office_d6 | 40000, 50000 | [RoboPRO @ `64840ce`](https://github.com/EAI-RSM/RoboPRO/commit/64840ce8ef7e468764cc725ba83c7492cd56615f) |
| `put_milktea_on_shelf` | 7 | test, train | office_clean, office_d10, office_d15, office_d6 | 40000, 40002, 50000 | [RoboPRO @ `64840ce`](https://github.com/EAI-RSM/RoboPRO/commit/64840ce8ef7e468764cc725ba83c7492cd56615f) |
| `put_phone_next_to_cube` | 2 | test, train | office_clean | 40000, 50000 | [RoboPRO @ `64840ce`](https://github.com/EAI-RSM/RoboPRO/commit/64840ce8ef7e468764cc725ba83c7492cd56615f) |
| `put_phone_on_holder` | 4 | test, train | office_clean, office_d10, office_d6 | 40000, 50000 | [RoboPRO @ `64840ce`](https://github.com/EAI-RSM/RoboPRO/commit/64840ce8ef7e468764cc725ba83c7492cd56615f) |

## Contents

| | |
|---|---|
| tasks | `drop_apple_in_bin_ks`, `move_pen_to_box`, `move_seal_next_to_box`, `move_seal_onto_table`, `pick_apple_from_bowl_ks`, `pick_bottle_from_fridge`, `pick_boxdrink_from_basket`, `put_bottle_in_basket`, `put_bottle_in_fridge`, `put_bread_on_board_ks`, `put_milktea_next_to_laptop`, `put_milktea_on_shelf`, `put_phone_next_to_cube`, `put_phone_on_holder` |
| scene seeds | 40000–50000 |
| search | `branch_once`, fan of 10, horizon 6 |
| policy | `pi05` — robopro @ 30000 |
| cameras | countertop_camera, right_camera, left_camera |
| action chunk | 50 steps |

| table | rows | files | columns |
|---|---|---|---|
| `nodes` | 2,165 | 44 | 11 |
| `steps` | 106,050 | 44 | 6 |

Outcomes, best to worst: `hard_success` solved it cleanly, `soft_success` solved it after
a collision, `soft_failure` missed, `hard_failure` missed and collided. `terminal` says
whether the episode had ended when the outcome was read — `tier` is an outcome only where
it is true.

## Loading it

```python
from datasets import load_dataset
nodes = load_dataset("mahgoobi/sim-branching-data", "nodes", split="train")   # the 50000+ collection seeds
test  = load_dataset("mahgoobi/sim-branching-data", "nodes", split="test")    # the benchmark's 40000+ bank
```

The library's splits are wired to the seed blocks, so `split="train"` gives the collection
seeds and `split="test"` the benchmark's evaluation bank — the same partition the `split`
column of `meta/searches` records, which stays the authority if the two ever disagree.

Every node carries its own state — poses, the robot's command, and one JPEG per camera —
so `nodes` alone answers most questions. `steps` is what happened between two nodes.
There is no video: `rewind video` builds one from these frames when you want to watch a
branch.

Data is partitioned as `data/<table>/task=<task>/<search_id>.parquet`, so one task is one
directory:

```python
from huggingface_hub import snapshot_download
snapshot_download("mahgoobi/sim-branching-data", repo_type="dataset",
                  allow_patterns=["meta/**", "data/*/task=drop_apple_in_bin_ks/*"])
```

Each run's config is stored verbatim at `meta/configs/<search_id>.yml`, so any run can be
repeated from the record itself.

Full format, and everything else: [https://gitlab.com/mahgoobi/rewind](https://gitlab.com/mahgoobi/rewind).