--- 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//task=/.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/.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).