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chore: remove usernames + use entrypoints in docs, comments & sample commands (#2988)

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This commit is contained in:
Steven Palma
2026-02-18 22:46:12 +01:00
committed by GitHub
parent bc38261321
commit 5f15232271
17 changed files with 97 additions and 97 deletions
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docs/source/earthrover_mini_plus.mdx
+1 -1
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@@ -185,7 +185,7 @@ echo $HF_USER
Use the standard recording command:
```bash
python src/lerobot/scripts/lerobot_record.py \
lerobot-record \
--robot.type=earthrover_mini_plus \
--teleop.type=keyboard_rover \
--dataset.repo_id=your_username/dataset_name \
docs/source/hope_jr.mdx
+5 -5
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@@ -224,7 +224,7 @@ lerobot-record \
--teleop.port=/dev/tty.usbmodem1201 \
--teleop.id=right \
--teleop.side=right \
--dataset.repo_id=nepyope/hand_record_test_with_video_data \
--dataset.repo_id=<USER>/hand_record_test_with_video_data \
--dataset.single_task="Hand recording test with video data" \
--dataset.num_episodes=1 \
--dataset.episode_time_s=5 \
@@ -241,7 +241,7 @@ lerobot-replay \
--robot.port=/dev/tty.usbmodem58760432281 \
--robot.id=right \
--robot.side=right \
--dataset.repo_id=nepyope/hand_record_test_with_camera \
--dataset.repo_id=<USER>/hand_record_test_with_camera \
--dataset.episode=0
```
@@ -249,13 +249,13 @@ lerobot-replay \
```bash
lerobot-train \
--dataset.repo_id=nepyope/hand_record_test_with_video_data \
--dataset.repo_id=<USER>/hand_record_test_with_video_data \
--policy.type=act \
--output_dir=outputs/train/hopejr_hand \
--job_name=hopejr \
--policy.device=mps \
--wandb.enable=true \
--policy.repo_id=nepyope/hand_test_policy
--policy.repo_id=<USER>/hand_test_policy
```
### Evaluate
@@ -270,7 +270,7 @@ lerobot-record \
--robot.side=right \
--robot.cameras='{"main": {"type": "opencv", "index_or_path": 0, "width": 640, "height": 480, "fps": 30}}' \
--display_data=false \
--dataset.repo_id=nepyope/eval_hopejr \
--dataset.repo_id=<USER>/eval_hopejr \
--dataset.single_task="Evaluate hopejr hand policy" \
--dataset.num_episodes=10 \
--policy.path=outputs/train/hopejr_hand/checkpoints/last/pretrained_model
docs/source/pi0.mdx
+1 -1
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@@ -60,7 +60,7 @@ policy.type=pi0
For training π₀, you can use the standard LeRobot training script with the appropriate configuration:
```bash
python src/lerobot/scripts/lerobot_train.py \
lerobot-train \
--dataset.repo_id=your_dataset \
--policy.type=pi0 \
--output_dir=./outputs/pi0_training \
docs/source/pi05.mdx
+1 -1
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@@ -56,7 +56,7 @@ policy.type=pi05
Here's a complete training command for finetuning the base π₀.₅ model on your own dataset:
```bash
python src/lerobot/scripts/lerobot_train.py\
lerobot-train \
--dataset.repo_id=your_dataset \
--policy.type=pi05 \
--output_dir=./outputs/pi05_training \
docs/source/sarm.mdx
+4 -4
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@@ -269,7 +269,7 @@ This generates visualizations showing video frames with subtask boundaries overl
Train with **no annotations** - uses linear progress from 0 to 1:
```bash
python src/lerobot/scripts/lerobot_train.py \
lerobot-train \
--dataset.repo_id=your-username/your-dataset \
--policy.type=sarm \
--policy.annotation_mode=single_stage \
@@ -288,7 +288,7 @@ python src/lerobot/scripts/lerobot_train.py \
Train with **dense annotations only** (sparse auto-generated):
```bash
python src/lerobot/scripts/lerobot_train.py \
lerobot-train \
--dataset.repo_id=your-username/your-dataset \
--policy.type=sarm \
--policy.annotation_mode=dense_only \
@@ -307,7 +307,7 @@ python src/lerobot/scripts/lerobot_train.py \
Train with **both sparse and dense annotations**:
```bash
python src/lerobot/scripts/lerobot_train.py \
lerobot-train \
--dataset.repo_id=your-username/your-dataset \
--policy.type=sarm \
--policy.annotation_mode=dual \
@@ -468,7 +468,7 @@ This script:
Once you have the progress file, train your policy with RA-BC weighting. The progress file is auto-detected from the dataset path (`sarm_progress.parquet`). Currently PI0, PI0.5 and SmolVLA are supported with RA-BC:
```bash
python src/lerobot/scripts/lerobot_train.py \
lerobot-train \
--dataset.repo_id=your-username/your-dataset \
--policy.type=pi0 \
--use_rabc=true \
docs/source/unitree_g1.mdx
+2 -2
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@@ -216,7 +216,7 @@ lerobot-teleoperate \
### Record Dataset in Simulation
```bash
python -m lerobot.scripts.lerobot_record \
lerobot-record \
--robot.type=unitree_g1 \
--robot.is_simulation=true \
--robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
@@ -266,7 +266,7 @@ lerobot-teleoperate \
### Record Dataset on Real Robot
```bash
python -m lerobot.scripts.lerobot_record \
lerobot-record \
--robot.type=unitree_g1 \
--robot.is_simulation=false \
--robot.cameras='{"global_view": {"type": "zmq", "server_address": "172.18.129.215", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
docs/source/walloss.mdx
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@@ -45,7 +45,7 @@ policy.type=wall_x
For training WallX, you can use the standard LeRobot training script with the appropriate configuration:
```bash
python src/lerobot/scripts/lerobot_train.py \
lerobot-train \
--dataset.repo_id=your_dataset \
--policy.type=wall_x \
--output_dir=./outputs/wallx_training \
docs/source/xvla.mdx
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@@ -154,7 +154,7 @@ lerobot-train \
```bash
lerobot-train \
--dataset.repo_id=pepijn223/bimanual-so100-handover-cube \
--dataset.repo_id=<USER>/bimanual-so100-handover-cube \
--output_dir=./outputs/xvla_bimanual \
--job_name=xvla_so101_training \
--policy.path="lerobot/xvla-base" \