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docs(groot): parameterize commands with env vars + fill LIBERO results

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- Introduce BASE_MODEL / DATASET_ID / REPO_ID / JOB_NAME / OUTPUT_DIR env vars
  in the training command and reuse OUTPUT_DIR + BASE_MODEL in the rollout cmd.
- Fill the LIBERO benchmark table with GR00T-LeRobot success rates
  (Spatial 94%, Object 98%, Goal 93%, LIBERO 10/Long 90%; avg 93.75%),
  drop the OSS column and XX placeholders. LeRobot-focused.
This commit is contained in:
nv-sachdevkartik
2026-07-02 12:49:35 +00:00
parent 9ec4530248
commit 62ff497ebc
1 changed files with 22 additions and 17 deletions
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docs/source/groot.mdx
+22 -17
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@@ -77,12 +77,19 @@ To use GR00T N1.7:
Here's a complete training command for finetuning the base GR00T model on your own dataset:
```bash
# Set these for your run
export BASE_MODEL=nvidia/GR00T-N1.7-3B
export DATASET_ID=sreetz-nv/so101-clean-up-vials-into-rack-50_20260628_131121
export REPO_ID=sreetz-nv/so101-clean-up-vials-into-rack-50-groot-n17-relact-bs64-20k-20260628_johnny_42
export JOB_NAME=so101-clean-up-vials-into-rack-50-groot-n17-relact-bs64-20k-20260628_johnny_42
export OUTPUT_DIR=outputs/train/$JOB_NAME
uv run lerobot-train \
--dataset.repo_id=sreetz-nv/so101-clean-up-vials-into-rack-50_20260628_131121 \
--dataset.repo_id=$DATASET_ID \
--dataset.image_transforms.enable=true \
--policy.type=groot \
--policy.device=cuda \
--policy.base_model_path=nvidia/GR00T-N1.7-3B \
--policy.base_model_path=$BASE_MODEL \
--policy.embodiment_tag=new_embodiment \
--policy.chunk_size=16 \
--policy.n_action_steps=16 \
@@ -91,7 +98,7 @@ uv run lerobot-train \
--policy.use_bf16=true \
--policy.use_flash_attention=true \
--policy.push_to_hub=true \
--policy.repo_id=sreetz-nv/so101-clean-up-vials-into-rack-50-groot-n17-relact-bs64-20k-20260628_johnny_42 \
--policy.repo_id=$REPO_ID \
--seed=42 \
--batch_size=64 \
--steps=20000 \
@@ -100,8 +107,8 @@ uv run lerobot-train \
--env_eval_freq=0 \
--eval_steps=0 \
--log_freq=100 \
--output_dir=outputs/train/sreetz-nv/so101-clean-up-vials-into-rack-50-groot-n17-relact-bs64-20k-20260628_johnny_42 \
--job_name=so101-clean-up-vials-into-rack-50-groot-n17-relact-bs64-20k-20260628_johnny_42
--output_dir=$OUTPUT_DIR \
--job_name=$JOB_NAME
```
## Performance Results
@@ -124,17 +131,15 @@ NVIDIA publishes GR00T N1.7 LIBERO checkpoints at [`nvidia/GR00T-N1.7-LIBERO`](h
| LIBERO Goal | `libero_goal` |
| LIBERO 10 | `libero_10` |
Preliminary LeRobot integration results:
Preliminary LeRobot integration results (GR00T-LeRobot, `eval.n_episodes >= 50` per suite):
| Suite | Status | Success rate | n_episodes |
| -------------- | ------ | -----------: | ---------: |
| LIBERO Spatial | ✓ | ~95% | XX |
| LIBERO Object | ✓ | XX% | XX |
| LIBERO Goal | ✓ | XX% | XX |
| LIBERO 10 | ✓ | XX% | XX |
| **Average** | ✓ | **XX%** | **XX** |
Replace the `XX` placeholders with final eval artifacts before merge.
| Suite | Success rate |
| ---------------------- | -----------: |
| LIBERO Spatial | 94% |
| LIBERO Object | 98% |
| LIBERO Goal | 93% |
| LIBERO 10 (Long) | 90% |
| **Average** | **93.75%** |
Download the suite checkpoint locally, then point `--policy.base_model_path` at the downloaded subdirectory. `--policy.path` is reserved for LeRobot checkpoints that contain a LeRobot `config.json` with a `type` field.
@@ -162,8 +167,8 @@ Once you have trained your model using your parameters you can run inference in
uv run lerobot-rollout \
--strategy.type=base \
--inference.type=rtc \
--policy.path=outputs/train/sreetz-nv/so101-clean-up-vials-into-rack-50-groot-n17-relact-bs64-20k-20260628_johnny_42/checkpoints/020000/pretrained_model/ \
--policy.base_model_path=nvidia/GR00T-N1.7-3B \
--policy.path=$OUTPUT_DIR/checkpoints/020000/pretrained_model/ \
--policy.base_model_path=$BASE_MODEL \
--robot.type=so101_follower \
--robot.port=/dev/ttyACM0 \
--robot.id=orange_andrew \