scoring_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.

650 searches · 13 tasks · 24,270 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://github.com/EAI-RSM/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	650 searches
test	the benchmark's own evaluation seeds (40000+) — what a scorer is measured on	not collected yet

from rewind.record.hub import index
runs  = index("mahgoobi/scoring_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	50	train	kitchens_clean	50000, 50001, 50002, 50003, 50004, 50005, 50006, 50007, 50008, 50009, 50010, 50011, 50012, 50013, 50014, 50015, 50016, 50017, 50018, 50019, 50020, 50021, 50022, 50023, 50024, 50025, 50026, 50027, 50028, 50029, 50030, 50031, 50032, 50033, 50034, 50035, 50036, 50037, 50038, 50039, 50040, 50041, 50042, 50043, 50044, 50045, 50046, 50047, 50048, 50049	RoboPRO @ 2a1adee
move_cup_put_pen_in_cup	50	train	study_clean	50000, 50001, 50002, 50003, 50004, 50005, 50006, 50007, 50008, 50009, 50010, 50011, 50012, 50013, 50014, 50015, 50016, 50017, 50018, 50019, 50020, 50021, 50022, 50023, 50024, 50025, 50026, 50027, 50028, 50029, 50030, 50031, 50032, 50033, 50034, 50035, 50036, 50037, 50038, 50039, 50040, 50041, 50042, 50043, 50044, 50045, 50046, 50047, 50048, 50049	RoboPRO @ 2a1adee
move_hamburger_onto_plate_ks	50	train	kitchens_clean	50000, 50001, 50003, 50005, 50006, 50007, 50009, 50010, 50011, 50012, 50013, 50015, 50018, 50019, 50020, 50021, 50022, 50023, 50026, 50027, 50028, 50029, 50030, 50032, 50033, 50034, 50035, 50036, 50037, 50041, 50043, 50044, 50045, 50047, 50048, 50049, 50051, 50052, 50053, 50054, 50055, 50056, 50057, 50058, 50059, 50060, 50061, 50062, 50064, 50065	RoboPRO @ 2a1adee
move_pen_to_box	50	train	study_clean	50000, 50001, 50002, 50003, 50004, 50005, 50006, 50007, 50008, 50009, 50010, 50011, 50012, 50013, 50014, 50015, 50016, 50017, 50018, 50019, 50020, 50021, 50022, 50023, 50024, 50025, 50026, 50027, 50028, 50029, 50030, 50031, 50032, 50033, 50034, 50035, 50036, 50037, 50038, 50039, 50040, 50041, 50042, 50043, 50044, 50045, 50046, 50047, 50048, 50049	RoboPRO @ 2a1adee
place_bowl_in_dishrack_ks	50	train	kitchens_clean	50000, 50001, 50002, 50003, 50004, 50005, 50006, 50007, 50008, 50009, 50010, 50011, 50012, 50013, 50014, 50015, 50016, 50017, 50018, 50019, 50020, 50021, 50022, 50023, 50024, 50025, 50026, 50027, 50028, 50029, 50030, 50031, 50032, 50033, 50034, 50035, 50036, 50037, 50038, 50039, 50040, 50041, 50042, 50043, 50044, 50045, 50046, 50047, 50048, 50049	RoboPRO @ 2a1adee
put_bottle_in_basket	50	train	kitchenl_clean	50000, 50001, 50002, 50003, 50004, 50005, 50006, 50007, 50008, 50009, 50010, 50011, 50012, 50013, 50014, 50015, 50016, 50017, 50018, 50019, 50020, 50021, 50022, 50023, 50024, 50025, 50027, 50028, 50029, 50030, 50031, 50032, 50033, 50034, 50035, 50036, 50037, 50038, 50039, 50040, 50041, 50042, 50043, 50044, 50045, 50046, 50047, 50048, 50049, 50050	RoboPRO @ 2a1adee
put_bottle_in_fridge	50	train	kitchenl_clean	50000, 50001, 50002, 50003, 50004, 50005, 50006, 50007, 50008, 50009, 50010, 50011, 50012, 50013, 50014, 50015, 50016, 50017, 50018, 50019, 50020, 50021, 50022, 50023, 50024, 50025, 50026, 50027, 50028, 50029, 50030, 50031, 50032, 50033, 50034, 50035, 50036, 50037, 50038, 50039, 50040, 50041, 50042, 50043, 50044, 50045, 50046, 50047, 50048, 50049	RoboPRO @ 2a1adee
put_can_in_cabinet	50	train	kitchenl_clean	50000, 50001, 50002, 50003, 50004, 50005, 50006, 50007, 50008, 50009, 50010, 50011, 50012, 50013, 50014, 50015, 50016, 50017, 50018, 50019, 50020, 50021, 50022, 50023, 50024, 50025, 50026, 50027, 50028, 50029, 50030, 50031, 50032, 50033, 50034, 50035, 50036, 50037, 50038, 50039, 50040, 50041, 50042, 50043, 50044, 50045, 50046, 50047, 50048, 50049	RoboPRO @ 2a1adee
put_cup_on_coaster	50	train	study_clean	50000, 50001, 50002, 50003, 50004, 50005, 50006, 50007, 50008, 50009, 50010, 50011, 50012, 50013, 50014, 50015, 50016, 50017, 50018, 50019, 50020, 50021, 50022, 50023, 50024, 50025, 50026, 50027, 50028, 50029, 50030, 50031, 50032, 50033, 50034, 50035, 50036, 50037, 50038, 50039, 50040, 50041, 50042, 50043, 50044, 50045, 50046, 50047, 50048, 50049	RoboPRO @ 2a1adee
put_milktea_on_shelf	50	train	office_clean	50000, 50001, 50002, 50003, 50004, 50005, 50006, 50007, 50008, 50009, 50010, 50011, 50012, 50013, 50014, 50015, 50016, 50017, 50018, 50019, 50020, 50021, 50022, 50023, 50024, 50025, 50026, 50027, 50028, 50029, 50030, 50031, 50032, 50033, 50034, 50035, 50036, 50037, 50038, 50039, 50040, 50041, 50042, 50043, 50044, 50045, 50046, 50047, 50048, 50049	RoboPRO @ 2a1adee
put_mouse_on_pad	50	train	office_clean	50000, 50001, 50002, 50003, 50004, 50005, 50006, 50007, 50008, 50009, 50010, 50011, 50012, 50013, 50014, 50015, 50016, 50017, 50018, 50019, 50020, 50021, 50022, 50023, 50024, 50025, 50026, 50027, 50028, 50029, 50030, 50031, 50032, 50033, 50034, 50035, 50036, 50037, 50038, 50039, 50040, 50041, 50042, 50043, 50044, 50045, 50046, 50047, 50048, 50049	RoboPRO @ 2a1adee
put_phone_on_holder	50	train	office_clean	50000, 50001, 50002, 50003, 50004, 50005, 50006, 50007, 50008, 50009, 50010, 50011, 50012, 50013, 50014, 50015, 50016, 50017, 50018, 50019, 50020, 50021, 50022, 50023, 50024, 50025, 50026, 50027, 50028, 50029, 50030, 50031, 50032, 50033, 50034, 50035, 50036, 50037, 50038, 50039, 50040, 50041, 50042, 50043, 50044, 50045, 50046, 50047, 50048, 50049	RoboPRO @ 2a1adee
put_stapler_next_to_mouse	50	train	office_clean	50000, 50001, 50002, 50003, 50004, 50005, 50006, 50007, 50008, 50009, 50010, 50011, 50012, 50013, 50014, 50015, 50016, 50017, 50018, 50019, 50020, 50021, 50022, 50023, 50024, 50025, 50026, 50027, 50028, 50029, 50030, 50031, 50032, 50033, 50034, 50035, 50036, 50037, 50038, 50039, 50040, 50041, 50042, 50043, 50044, 50045, 50046, 50047, 50048, 50049	RoboPRO @ 2a1adee

Contents

tasks	drop_apple_in_bin_ks, move_cup_put_pen_in_cup, move_hamburger_onto_plate_ks, move_pen_to_box, place_bowl_in_dishrack_ks, put_bottle_in_basket, put_bottle_in_fridge, put_can_in_cabinet, put_cup_on_coaster, put_milktea_on_shelf, put_mouse_on_pad, put_phone_on_holder, put_stapler_next_to_mouse
scene seeds	50000–50065
search	branch_once, fan of 5, horizon 12
policy	roboresearch_policy — None @ 0
cameras	countertop_camera, right_camera, left_camera
action chunk	50 steps

table	rows	files	columns
nodes	24,270	650	10
steps	1,181,000	650	5

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, and only those are counted here.

outcome	branches
hard_success	2,093
soft_success	20
soft_failure	1,076
hard_failure	61

Loading it

from datasets import load_dataset
train = load_dataset("mahgoobi/scoring_data", "nodes", split="train")   # the collection seeds

The library's splits ARE the split column of meta/searches: the frontmatter above names each run's shard under the block that column puts it in, so the two cannot drift. Only the blocks this repo actually holds are declared.

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:

from huggingface_hub import snapshot_download
snapshot_download("mahgoobi/scoring_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://github.com/EAI-RSM/rewind.