| --- |
| license: apache-2.0 |
| base_model: nvidia/GR00T-N1.7-3B |
| library_name: gr00t |
| pipeline_tag: robotics |
| tags: [robotics, vla, gr00t, isaac-gr00t, decent-vla, cube-stacking, so-101, federated] |
| --- |
| |
| # GR00T N1.7 CubeStack — FedAvg 3-client (global) |
|
|
| Federated global model: FedAvg over 3 colour-pair clients, **50 rounds x 250 local steps**, batch 32. Every round aggregated all 3 clients (`updates=3`). |
|
|
| Trained on Isambard-AI (GH200) with [decent-vla](https://github.com/kevinDuan1/decent-vla). |
|
|
| **Scope: action-head-only** — the Cosmos-Reason2 backbone is frozen |
| (`tune_llm`/`tune_visual` false); the ~1.62B DiT action head plus the embodiment |
| projector are trained. That is the standard NVIDIA new-embodiment recipe: the SO-101 |
| projector slot ships at random init, so `tune_projector` must stay on. |
|
|
| **Embodiment:** SO-101 (`NEW_EMBODIMENT`), state/action 6-D `single_arm`+`gripper`, |
| cameras `front`/`wrist`. The `DecentVLA/Cube_*` datasets are LeRobot v3.0, which the |
| GR00T loader cannot read, so they were converted to v2.1 and the video pre-scaled to |
| shortest edge 256 (GR00T resizes to exactly that internally; it cut decode ~3.9x). |
|
|
| **Partition:** the SAME colour-pair non-IID split as the pi0.5 and SmolVLA CubeStack |
| 3-client studies — c0 harry Green/Orange, c1 zhekai Green/Blue, c2 kevin Orange/Blue, |
| each client blind to the third colour. |
|
|
| **Caveat worth knowing:** the two kevin prompts are worded differently from the other |
| four ("Put orange cube on the blue cube." vs "Put the orange cube on top of the blue |
| cube."). Kept verbatim so this arm trains on exactly the strings the pi0.5 and SmolVLA |
| arms saw. |
|
|
| Both checkpoints hold the SAME parameter set: **537 tensors, 1.6205 B params, all |
| `action_head.*`** (verified by reading the tensors) — i.e. the frozen-backbone scope. |
| |
| Checkpoint wrapper differs by arm, because they came off different code paths — load with |
| decent-vla `--resume`, not `from_pretrained`: |
| * FL `server_round_0050.pt` — `{"state": {...}, "round": 49}` (server aggregate; `round` is 0-indexed). |
| * Centralized `round_0075.pt` — `{"state_dict", "include", "extra", "embodiment", "backend"}` |
| (the LocalRunner end-of-run save, which also records embodiment + backend metadata). |
| Note there is no `server_round_0075.pt`: periodic saves ran every 10 rounds, and 75 is |
| not a multiple of 10, so round 75 exists only as this end-of-run file. |
| |
| ## Running these weights (experiment_cfg/) |
| |
| The checkpoint holds ONLY `action_head.*` (537 tensors, 1.6205 B). Three things GR00T |
| derives from the training dataset are not in it and not in the public N1.7 base repo, so |
| they are published here under `experiment_cfg/`: |
| |
| | file | what it is | |
| |---|---| |
| | `embodiment_id.json` | the projector-slot mapping used in this run | |
| | `processor_config.json` | modality config (state/action index ranges, video keys) + image/state processor settings | |
| | `statistics.json` | state/action normalization statistics | |
| |
| **Embodiment slot = 10.** `new_embodiment` is absent from the base repo's |
| `embodiment_id.json`; the value comes from `EMBODIMENT_TAG_TO_PROJECTOR_INDEX` in |
| `gr00t/model/gr00t_n1d7/processing_gr00t_n1d7.py`. Verified against the weights, not just |
| that table: diffing this checkpoint's 14 embodiment-indexed tensors against the base head |
| puts **100.0% of the drift in slot 10 and exactly 0.0 in all 31 other slots** (see |
| `experiment_cfg/slot_drift.json`). Slot 0 is untrained — falling back to it runs without |
| error and produces meaningless actions. |
| |
| Modality (from `processor_config.json`): video keys `front`, `wrist`; state and action |
| `single_arm` (0-5) + `gripper` (5-6); action horizon 16; `shortest_image_edge` 256. |
| |
| The training config for this run is included as `experiment_cfg/train_config.yaml`. |
| |
| ### Which statistics for the federated model? |
| |
| Our GR00T adapter wires normalization **per client partition**, so each FL client trained |
| against the statistics of its own two repos — there is no single "global" normalizer that |
| training produced. All four are published: |
| |
| * `statistics.json` — **pooled over all 6 repos** (identical to the centralized arm). Use |
| this one for a single-number evaluation of the global model: it is also what the pi0.5 |
| and SmolVLA CubeStack FL models carry, so it keeps the three policies comparable. |
| * `statistics_client{0,1,2}.json` — what each client actually trained with |
| (c0 harry Green/Orange, c1 zhekai Green/Blue, c2 kevin Orange/Blue). |
| |
| They differ substantially — `action.gripper.mean` is 4.92 / 8.05 / 19.78 per client vs |
| 10.92 pooled — so this choice changes results. Note this is a real difference from the |
| pi0.5 and SmolVLA CubeStack arms, whose lerobot adapter pooled the statistics at training |
| time; disclose it in any cross-policy table. |
| |
