| --- |
| license: apache-2.0 |
| task_categories: [robotics] |
| tags: [robotics, manipulation, benchmark, DOMINO, RoboTwin, reproducibility] |
| --- |
| |
| # DOMINO fixed-episode evaluation manifests |
|
|
| Pre-screened episode sets for the [DOMINO](https://github.com/H-EmbodVis/DOMINO) benchmark, `demo_clean_dynamic`, seed 0. |
|
|
| ## Why |
|
|
| DOMINO's default protocol screens candidate seeds online with an RRT expert planner |
| until it finds 100 solvable episodes. That planner is **stochastic**, so two runs accept |
| different episode sets and two policies end up measured on different physics. |
|
|
| A manifest pins the accepted seeds **and** each episode's dynamic-motion state -- |
| target object, start/end position, velocity vector, duration, and an RNG snapshot |
| (numpy, python, torch, torch_cuda). Every policy then sees identical episodes. |
| |
| Producing these took roughly 12,414 expert-planner rollouts (~6 GPU-hours). |
| |
| ## Use |
| |
| ```bash |
| git clone https://github.com/H-EmbodVis/DOMINO.git && cd DOMINO |
| |
| # --include is important: without it, this repo's own README.md would |
| # overwrite DOMINO's README.md in your clone. |
| hf download <REPO> --repo-type dataset --include 'eval_manifest/*' --local-dir . |
| # (huggingface_hub < 0.34 spells the command `huggingface-cli download`) |
| |
| python script/eval_policy.py --config <deploy_policy.yml> --overrides \ |
| --task_name adjust_bottle --task_config demo_clean_dynamic \ |
| --episode_manifest eval_manifest/adjust_bottle/demo_clean_dynamic/seed0.pkl |
| ``` |
| |
| `eval_policy.py` validates `task_name`, `task_config`, `embodiment`, `use_dynamic`, |
| `dynamic_level` and `dynamic_coefficient` against the manifest and refuses to run on a |
| mismatch, so a manifest cannot silently be paired with the wrong configuration. |
| |
| ## Provenance |
| |
| - DOMINO commit: `22cd288865827cb932003c877cf2870fe5ca22e6` |
| - manifest `format_version`: 1 |
| - embodiment: `['aloha-agilex']` |
| - `use_dynamic=True`, `dynamic_level=1`, `dynamic_coefficient=0.1` |
| - episodes per task: 100 |
| - candidate seeds start at 100000 |
| |
| ## Caveats |
| |
| - Python **pickles** carrying numpy RNG state. Load with a compatible numpy (DOMINO pins 1.26.4). `EpisodeManifest.load` rejects any other `format_version`. |
| - Physics replay is **not bit-exact across machines**; mm-level contact differences remain. What these remove is episode-set drift and initial-state drift, the dominant variance sources. |
| - The `.json` beside each `.pkl` is a human-readable summary. Evaluation reads only the `.pkl`. |
| |
| ## Contents |
| |
| 35 tasks, 3,500 episodes, 12,414 candidate seeds screened (28% overall acceptance). |
| |
| Sorted by acceptance rate -- low values mean the expert planner rarely solves that |
| scene, so those manifests were the expensive ones to produce. |
| |
| | task | accepted | candidates | acceptance | seed range | |
| |---|---:|---:|---:|---| |
| | `put_object_cabinet` | 100 | 2004 | 5% | 100030–102003 | |
| | `scan_object` | 100 | 1335 | 7% | 100014–101334 | |
| | `place_bread_basket` | 100 | 1019 | 10% | 100003–101018 | |
| | `dump_bin_bigbin` | 100 | 780 | 13% | 100003–100779 | |
| | `move_playingcard_away` | 100 | 626 | 16% | 100003–100625 | |
| | `place_shoe` | 100 | 614 | 16% | 100000–100613 | |
| | `place_object_basket` | 100 | 419 | 24% | 100000–100418 | |
| | `place_bread_skillet` | 100 | 321 | 31% | 100002–100320 | |
| | `place_a2b_left` | 100 | 318 | 31% | 100004–100317 | |
| | `shake_bottle_horizontally` | 100 | 313 | 32% | 100004–100312 | |
| | `handover_block` | 100 | 299 | 33% | 100002–100298 | |
| | `shake_bottle` | 100 | 299 | 33% | 100000–100298 | |
| | `place_a2b_right` | 100 | 294 | 34% | 100002–100293 | |
| | `put_bottles_dustbin` | 100 | 271 | 37% | 100000–100270 | |
| | `place_can_basket` | 100 | 235 | 43% | 100002–100234 | |
| | `place_fan` | 100 | 228 | 44% | 100002–100227 | |
| | `beat_block_hammer` | 100 | 224 | 45% | 100003–100223 | |
| | `hanging_mug` | 100 | 224 | 45% | 100000–100223 | |
| | `handover_mic` | 100 | 223 | 45% | 100000–100222 | |
| | `grab_roller` | 100 | 197 | 51% | 100000–100196 | |
| | `move_can_pot` | 100 | 180 | 56% | 100000–100179 | |
| | `rotate_qrcode` | 100 | 178 | 56% | 100002–100177 | |
| | `adjust_bottle` | 100 | 172 | 58% | 100000–100171 | |
| | `click_alarmclock` | 100 | 171 | 58% | 100000–100170 | |
| | `stamp_seal` | 100 | 169 | 59% | 100000–100168 | |
| | `move_pillbottle_pad` | 100 | 157 | 64% | 100002–100156 | |
| | `place_mouse_pad` | 100 | 155 | 65% | 100000–100154 | |
| | `press_stapler` | 100 | 155 | 65% | 100000–100154 | |
| | `place_object_scale` | 100 | 137 | 73% | 100000–100136 | |
| | `place_phone_stand` | 100 | 128 | 78% | 100000–100127 | |
| | `place_container_plate` | 100 | 122 | 82% | 100000–100121 | |
| | `move_stapler_pad` | 100 | 119 | 84% | 100002–100118 | |
| | `place_empty_cup` | 100 | 117 | 85% | 100000–100116 | |
| | `place_object_stand` | 100 | 109 | 92% | 100000–100108 | |
| | `click_bell` | 100 | 102 | 98% | 100000–100101 | |
| |
| ## License |
| |
| Apache-2.0, matching DOMINO. Please cite the DOMINO paper if you use these. |
| |
