File size: 7,720 Bytes
577f465 1844f88 577f465 1844f88 577f465 1844f88 577f465 007b5cb 577f465 5d96224 007b5cb 577f465 be24f2e 5d96224 be24f2e c443e15 be24f2e 31f5c3b 9e25403 31f5c3b 577f465 5d96224 007b5cb 577f465 5d96224 577f465 1844f88 577f465 1844f88 c443e15 007b5cb 577f465 c443e15 577f465 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | ---
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).
|