| # Actual evaluation method and results |
|
|
| The historical benchmark is `robotwin-eval-subset-v2-seen-unseen`, fixed subset commit |
| `b5abe512dafd400736c6dca5905f2fd262dd9029`: 50 tasks × 10 clean/seen + 10 randomized/unseen |
| episodes, 1000 episodes per model. The same 1000 episode IDs and initial RGB/public-state |
| hashes match across endpoints. Accepted policy failures are included; infrastructure retries |
| are excluded. Evaluation uses unseen instructions, not unseen-instruction training. |
| All tasks are present in the all-clean training set. Seen/unseen refers to instruction pools; |
| clean/seen versus randomized/unseen changes both visual conditions and instruction pools. |
| These results concern the original fixed-table runs, not later low-table adaptations. |
|
|
| | Model | Clean / seen | Randomized / unseen | Overall | |
| |---|---:|---:|---:| |
| | ep2 | 381/500 (76.2%) | 204/500 (40.8%) | 585/1000 (58.5%) | |
| | ep6 | 416/500 (83.2%) | 178/500 (35.6%) | 594/1000 (59.4%) | |
|
|
| Per-task counts, completed/planned counts, success rates, worker counts and measured episode |
| wall times are in `RESULTS.json`. Wall times are specific to the actual distributed setup, |
| not hardware-independent model latency. These are historical results, not current service status. |
|
|
| Model inference used F32 weights/main path, eager action attention, compile=false, tf32=false, |
| autocast=false. Official FA2 vision casts FP32 Q/K/V to BF16 internally and restores output dtype. |
| E5 causal history is reset per episode/session, uses only confirmed executed command prefixes, |
| and prior public drive-target state observations. It is not measured-qpos history. |
|
|
| The checkpoint requires custom runtime loading, the original normalization, three camera inputs, |
| 14-dimensional execution commands and baseline dependencies. Core runtime source is preserved |
| from original training archives. `scripts/e5_rollout_history.py` implements the causal history contract. |
| Use `HARDWARE.md` for actual inference/rendering machines and precision boundaries. |
|
|
| Exact original server, transport, renderer worker and subset verifier source from both evaluations is included and hashed in `SOURCE_MANIFEST.json`. |
|
|
| Exact end-to-end rollout reproduction still requires the fixed subset, compatible RoboTwin |
| simulation assets and matching server/renderer integration. No generic upstream evaluation |
| script is represented as the exact 1000-episode harness. No new rollout was run for this release. |
| 原始记录保存在本地。 |
|
|
| ## Portable entry |
|
|
| Download the selected public checkpoint, including `training_config.yaml`, to `checkpoints/ep2` |
| or `checkpoints/ep6`. The release identity adapter verifies all six original shard hashes, |
| the index, sanitized model/training config and normalization before loading. It replaces |
| only checks tied to private training records. The loader now reads `training_config.yaml` |
| beside the shards. `SOURCE_MANIFEST.json` lists these changes and retains both original |
| registry hashes. The earlier training-archive rollout-history version is retained under |
| `training_reference/`; the active version is the exact source used by both evaluations. |
|
|
| Install the CUDA inference dependencies; keep Qwen3-VL under |
| `checkpoints/dependencies/Qwen3-VL-4B-Instruct`. Place compatible RoboTwin source/assets in |
| `third_party/RoboTwin`. Original warmup requires the clean adjust_bottle episode0 HDF5 under |
| `data/raw/adjust_bottle/aloha-agilex_clean_50/data/episode0.hdf5`. |
| The fixed subset must be a checksummed Git checkout. A public mirror is |
| [ar-mine/robotwin-eval-subset-v2-seen-unseen](https://huggingface.co/datasets/ar-mine/robotwin-eval-subset-v2-seen-unseen/tree/73277c2022be224d3afb195b503a3cff56ded46d), |
| at HF revision `73277c2022be224d3afb195b503a3cff56ded46d`. Its package SHA256SUMS is byte-identical |
| to the original benchmark (SHA256 `a8a41f7c5162732cce287e77628840bf754f631825d3a6a343d51c648f932eb9`). |
| The wrapper accepts either the original Git commit above or this fixed public mirror commit, |
| verifies the exact checksum-file hash and all package files, and records the actual checkout |
| commit used. Set `SUBSET_ROOT` to `benchmarks/robotwin_eval_subset_v2_seen_unseen` inside that |
| checkout and `RUN_DIR` to a fresh local result directory. Configure the |
| already-established transport so the renderer can reach the inference server on localhost |
| port 9340. The wrapper creates no SSH tunnels, scheduler allocations or remote jobs. |
|
|
| On the inference machine (A100), after selecting its GPU through `CUDA_VISIBLE_DEVICES`: |
|
|
| ```bash |
| python scripts/release_eval.py server --epoch ep6 --output "$RUN_DIR/server" --port 9340 --run |
| ``` |
|
|
| On the renderer (RTX 5090), set `EVAL_GPU_UUID` and `CUDA_VISIBLE_DEVICES` to the same allocated |
| render GPU UUID. Execute an episode from the fixed subset: |
|
|
| ```bash |
| python scripts/release_eval.py episode --epoch ep6 --subset "$SUBSET_ROOT" --task adjust_bottle --setting clean --index 0 --output "$RUN_DIR/adjust_bottle_clean_0" --port 9340 --run |
| ``` |
|
|
| Omit `--run` to validate/print a plan without starting a GPU process or service. For epoch2 |
| choose `--epoch ep2` on both sides. The original worker applies per-episode seeds, RT rendering, |
| confirmed-action and public-state history, session reset and strict server-identity checks. |
| The wrapper runs one episode at a time; full1000 orchestration, resumability and infrastructure |
| retry accounting remain the executor's responsibility. Do not compare an incomplete run with |
| the historical full1000 table. Portable entries received CPU validation only; target-GPU |
| loading, the two-episode causal-history smoke, and full benchmark reproduction remain pending. |
|
|
