tangger/lerobot
1
0
Fork 1
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

chore(docs): prioritize use of entry points in docs + fix nightly badge (#1692)

Browse Source 
* chore(docs): fix typo in nightly badge

* chore(docs): prioritize the use of entrypoints for consistency
This commit is contained in:
Steven Palma
2025-08-07 14:25:44 +02:00
committed by GitHub
parent c66cd40176
commit ce3b9f627e
31 changed files with 105 additions and 107 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
.github/PULL_REQUEST_TEMPLATE.md vendored
View File

@@ -30,7 +30,7 @@ pytest -sx tests/test_stuff.py::test_something
```
```bash
python -m lerobot.scripts.train --some.option=true
lerobot-train --some.option=true
```
## SECTION TO REMOVE BEFORE SUBMITTING YOUR PR

18
Makefile
View File

@@ -44,7 +44,7 @@ test-end-to-end:
${MAKE} DEVICE=$(DEVICE) test-smolvla-ete-eval
test-act-ete-train:
python -m lerobot.scripts.train \
lerobot-train \
--policy.type=act \
--policy.dim_model=64 \
--policy.n_action_steps=20 \
@@ -68,12 +68,12 @@ test-act-ete-train:
--output_dir=tests/outputs/act/
test-act-ete-train-resume:
python -m lerobot.scripts.train \
lerobot-train \
--config_path=tests/outputs/act/checkpoints/000002/pretrained_model/train_config.json \
--resume=true
test-act-ete-eval:
python -m lerobot.scripts.eval \
lerobot-eval \
--policy.path=tests/outputs/act/checkpoints/000004/pretrained_model \
--policy.device=$(DEVICE) \
--env.type=aloha \
@@ -82,7 +82,7 @@ test-act-ete-eval:
--eval.batch_size=1
test-diffusion-ete-train:
python -m lerobot.scripts.train \
lerobot-train \
--policy.type=diffusion \
--policy.down_dims='[64,128,256]' \
--policy.diffusion_step_embed_dim=32 \
@@ -106,7 +106,7 @@ test-diffusion-ete-train:
--output_dir=tests/outputs/diffusion/
test-diffusion-ete-eval:
python -m lerobot.scripts.eval \
lerobot-eval \
--policy.path=tests/outputs/diffusion/checkpoints/000002/pretrained_model \
--policy.device=$(DEVICE) \
--env.type=pusht \
@@ -115,7 +115,7 @@ test-diffusion-ete-eval:
--eval.batch_size=1
test-tdmpc-ete-train:
python -m lerobot.scripts.train \
lerobot-train \
--policy.type=tdmpc \
--policy.device=$(DEVICE) \
--policy.push_to_hub=false \
@@ -137,7 +137,7 @@ test-tdmpc-ete-train:
--output_dir=tests/outputs/tdmpc/
test-tdmpc-ete-eval:
python -m lerobot.scripts.eval \
lerobot-eval \
--policy.path=tests/outputs/tdmpc/checkpoints/000002/pretrained_model \
--policy.device=$(DEVICE) \
--env.type=xarm \
@@ -148,7 +148,7 @@ test-tdmpc-ete-eval:
test-smolvla-ete-train:
python -m lerobot.scripts.train \
lerobot-train \
--policy.type=smolvla \
--policy.n_action_steps=20 \
--policy.chunk_size=20 \
@@ -171,7 +171,7 @@ test-smolvla-ete-train:
--output_dir=tests/outputs/smolvla/
test-smolvla-ete-eval:
python -m lerobot.scripts.eval \
lerobot-eval \
--policy.path=tests/outputs/smolvla/checkpoints/000004/pretrained_model \
--policy.device=$(DEVICE) \
--env.type=aloha \

12
README.md
View File

@@ -6,7 +6,7 @@
<div align="center">
[![Tests](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/nighty.yml?query=branch%3Amain)
[![Tests](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml?query=branch%3Amain)
[![Python versions](https://img.shields.io/pypi/pyversions/lerobot)](https://www.python.org/downloads/)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/huggingface/lerobot/blob/main/LICENSE)
[![Status](https://img.shields.io/pypi/status/lerobot)](https://pypi.org/project/lerobot/)
@@ -276,7 +276,7 @@ Check out [example 2](https://github.com/huggingface/lerobot/blob/main/examples/
We also provide a more capable script to parallelize the evaluation over multiple environments during the same rollout. Here is an example with a pretrained model hosted on [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht):
```bash
python -m lerobot.scripts.eval \
lerobot-eval \
--policy.path=lerobot/diffusion_pusht \
--env.type=pusht \
--eval.batch_size=10 \
@@ -288,10 +288,10 @@ python -m lerobot.scripts.eval \
Note: After training your own policy, you can re-evaluate the checkpoints with:
```bash
python -m lerobot.scripts.eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
lerobot-eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
```
See `python -m lerobot.scripts.eval --help` for more instructions.
See `lerobot-eval --help` for more instructions.
### Train your own policy
@@ -303,7 +303,7 @@ A link to the wandb logs for the run will also show up in yellow in your termina
\<img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/wandb.png" alt="WandB logs example"\>
Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python -m lerobot.scripts.eval --help` for more instructions.
Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `lerobot-eval --help` for more instructions.
#### Reproduce state-of-the-art (SOTA)
@@ -311,7 +311,7 @@ We provide some pretrained policies on our [hub page](https://huggingface.co/ler
You can reproduce their training by loading the config from their run. Simply running:
```bash
python -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
lerobot-train --config_path=lerobot/diffusion_pusht
```
reproduces SOTA results for Diffusion Policy on the PushT task.

2
docs/source/cameras.mdx
View File

@@ -9,7 +9,7 @@ To instantiate a camera, you need a camera identifier. This identifier might cha
To find the camera indices of the cameras plugged into your system, run the following script:
```bash
python -m lerobot.find_cameras opencv # or realsense for Intel Realsense cameras
lerobot-find-cameras opencv # or realsense for Intel Realsense cameras
```
The output will look something like this if you have two cameras connected:

4
docs/source/hilserl.mdx
View File

@@ -412,7 +412,7 @@ Example configuration for training the [reward classifier](https://huggingface.c
To train the classifier, use the `train.py` script with your configuration:
```bash
python -m lerobot.scripts.train --config_path path/to/reward_classifier_train_config.json
lerobot-train --config_path path/to/reward_classifier_train_config.json
```
**Deploying and Testing the Model**
@@ -458,7 +458,7 @@ The reward classifier will automatically provide rewards based on the visual inp
3. **Train the classifier**:
```bash
python -m lerobot.scripts.train --config_path src/lerobot/configs/reward_classifier_train_config.json
lerobot-train --config_path src/lerobot/configs/reward_classifier_train_config.json
```
4. **Test the classifier**:

22
docs/source/hope_jr.mdx
View File

@@ -19,7 +19,7 @@ pip install -e ".[hopejr]"
Before starting calibration and operation, you need to identify the USB ports for each HopeJR component. Run this script to find the USB ports for the arm, hand, glove, and exoskeleton:
```bash
python -m lerobot.find_port
lerobot-find-port
```
This will display the available USB ports and their associated devices. Make note of the port paths (e.g., `/dev/tty.usbmodem58760433331`, `/dev/tty.usbmodem11301`) as you'll need to specify them in the `--robot.port` and `--teleop.port` parameters when recording data, replaying episodes, or running teleoperation scripts.
@@ -31,7 +31,7 @@ Before performing teleoperation, HopeJR's limbs need to be calibrated. Calibrati
### 1.1 Calibrate Robot Hand
```bash
python -m lerobot.calibrate \
lerobot-calibrate \
--robot.type=hope_jr_hand \
--robot.port=/dev/tty.usbmodem58760432281 \
--robot.id=blue \
@@ -81,7 +81,7 @@ Once you have set the appropriate boundaries for all joints, click "Save" to sav
### 1.2 Calibrate Teleoperator Glove
```bash
python -m lerobot.calibrate \
lerobot-calibrate \
--teleop.type=homunculus_glove \
--teleop.port=/dev/tty.usbmodem11201 \
--teleop.id=red \
@@ -120,7 +120,7 @@ Once calibration is complete, the system will save the calibration to `/Users/yo
### 1.3 Calibrate Robot Arm
```bash
python -m lerobot.calibrate \
lerobot-calibrate \
--robot.type=hope_jr_arm \
--robot.port=/dev/tty.usbserial-1110 \
--robot.id=white
@@ -146,7 +146,7 @@ Use the calibration interface to set the range boundaries for each joint. Move e
### 1.4 Calibrate Teleoperator Exoskeleton
```bash
python -m lerobot.calibrate \
lerobot-calibrate \
--teleop.type=homunculus_arm \
--teleop.port=/dev/tty.usbmodem11201 \
--teleop.id=black
@@ -178,7 +178,7 @@ Due to global variable conflicts in the Feetech middleware, teleoperation for ar
### Hand
```bash
python -m lerobot.teleoperate \
lerobot-teleoperate \
--robot.type=hope_jr_hand \
--robot.port=/dev/tty.usbmodem58760432281 \
--robot.id=blue \
@@ -194,7 +194,7 @@ python -m lerobot.teleoperate \
### Arm
```bash
python -m lerobot.teleoperate \
lerobot-teleoperate \
--robot.type=hope_jr_arm \
--robot.port=/dev/tty.usbserial-1110 \
--robot.id=white \
@@ -214,7 +214,7 @@ Record, Replay and Train with Hope-JR is still experimental.
This step records the dataset, which can be seen as an example [here](https://huggingface.co/datasets/nepyope/hand_record_test_with_video_data/settings).
```bash
python -m lerobot.record \
lerobot-record \
--robot.type=hope_jr_hand \
--robot.port=/dev/tty.usbmodem58760432281 \
--robot.id=right \
@@ -236,7 +236,7 @@ python -m lerobot.record \
### Replay
```bash
python -m lerobot.replay \
lerobot-replay \
--robot.type=hope_jr_hand \
--robot.port=/dev/tty.usbmodem58760432281 \
--robot.id=right \
@@ -248,7 +248,7 @@ python -m lerobot.replay \
### Train
```bash
python -m lerobot.scripts.train \
lerobot-train \
--dataset.repo_id=nepyope/hand_record_test_with_video_data \
--policy.type=act \
--output_dir=outputs/train/hopejr_hand \
@@ -263,7 +263,7 @@ python -m lerobot.scripts.train \
This training run can be viewed as an example [here](https://wandb.ai/tino/lerobot/runs/rp0k8zvw?nw=nwusertino).
```bash
python -m lerobot.record \
lerobot-record \
--robot.type=hope_jr_hand \
--robot.port=/dev/tty.usbmodem58760432281 \
--robot.id=right \

16
docs/source/il_robots.mdx
View File

@@ -45,7 +45,7 @@ Note that the `id` associated with a robot is used to store the calibration file
<hfoptions id="teleoperate_so101">
<hfoption id="Command">
```bash
python -m lerobot.teleoperate \
lerobot-teleoperate \
--robot.type=so101_follower \
--robot.port=/dev/tty.usbmodem58760431541 \
--robot.id=my_awesome_follower_arm \
@@ -101,7 +101,7 @@ With `rerun`, you can teleoperate again while simultaneously visualizing the cam
<hfoptions id="teleoperate_koch_camera">
<hfoption id="Command">
```bash
python -m lerobot.teleoperate \
lerobot-teleoperate \
--robot.type=koch_follower \
--robot.port=/dev/tty.usbmodem58760431541 \
--robot.id=my_awesome_follower_arm \
@@ -174,7 +174,7 @@ Now you can record a dataset. To record 5 episodes and upload your dataset to th
<hfoptions id="record">
<hfoption id="Command">
```bash
python -m lerobot.record \
lerobot-record \
--robot.type=so101_follower \
--robot.port=/dev/tty.usbmodem585A0076841 \
--robot.id=my_awesome_follower_arm \
@@ -376,7 +376,7 @@ You can replay the first episode on your robot with either the command below or
<hfoptions id="replay">
<hfoption id="Command">
```bash
python -m lerobot.replay \
lerobot-replay \
--robot.type=so101_follower \
--robot.port=/dev/tty.usbmodem58760431541 \
--robot.id=my_awesome_follower_arm \
@@ -428,10 +428,10 @@ Your robot should replicate movements similar to those you recorded. For example
## Train a policy
To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
To train a policy to control your robot, use the [`lerobot-train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
```bash
python -m lerobot.scripts.train \
lerobot-train \
--dataset.repo_id=${HF_USER}/so101_test \
--policy.type=act \
--output_dir=outputs/train/act_so101_test \
@@ -453,7 +453,7 @@ Training should take several hours. You will find checkpoints in `outputs/train/
To resume training from a checkpoint, below is an example command to resume from `last` checkpoint of the `act_so101_test` policy:
```bash
python -m lerobot.scripts.train \
lerobot-train \
--config_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model/train_config.json \
--resume=true
```
@@ -490,7 +490,7 @@ You can use the `record` script from [`lerobot/record.py`](https://github.com/hu
<hfoptions id="eval">
<hfoption id="Command">
```bash
python -m lerobot.record \
lerobot-record \
--robot.type=so100_follower \
--robot.port=/dev/ttyACM1 \
--robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \

4
docs/source/il_sim.mdx
View File

@@ -96,10 +96,10 @@ If you uploaded your dataset to the hub you can [visualize your dataset online](
## Train a policy
To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
To train a policy to control your robot, use the [`lerobot-train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
```bash
python -m lerobot.scripts.train \
lerobot-train \
--dataset.repo_id=${HF_USER}/il_gym \
--policy.type=act \
--output_dir=outputs/train/il_sim_test \

10
docs/source/koch.mdx
View File

@@ -31,7 +31,7 @@ pip install -e ".[dynamixel]"
To find the port for each bus servo adapter, run this script:
```bash
python -m lerobot.find_port
lerobot-find-port
```
<hfoptions id="example">
@@ -98,7 +98,7 @@ For a visual reference on how to set the motor ids please refer to [this video](
<hfoption id="Command">
```bash
python -m lerobot.setup_motors \
lerobot-setup-motors \
--robot.type=koch_follower \
--robot.port=/dev/tty.usbmodem575E0031751 # <- paste here the port found at previous step
```
@@ -174,7 +174,7 @@ Do the same steps for the leader arm but modify the command or script accordingl
<hfoption id="Command">
```bash
python -m lerobot.setup_motors \
lerobot-setup-motors \
--teleop.type=koch_leader \
--teleop.port=/dev/tty.usbmodem575E0031751 \ # <- paste here the port found at previous step
```
@@ -211,7 +211,7 @@ Run the following command or API example to calibrate the follower arm:
<hfoption id="Command">
```bash
python -m lerobot.calibrate \
lerobot-calibrate \
--robot.type=koch_follower \
--robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
--robot.id=my_awesome_follower_arm # <- Give the robot a unique name
@@ -249,7 +249,7 @@ Do the same steps to calibrate the leader arm, run the following command or API
<hfoption id="Command">
```bash
python -m lerobot.calibrate \
lerobot-calibrate \
--teleop.type=koch_leader \
--teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
--teleop.id=my_awesome_leader_arm # <- Give the robot a unique name

8
docs/source/lekiwi.mdx
View File

@@ -60,7 +60,7 @@ First, we will assemble the two SO100/SO101 arms. One to attach to the mobile ba
To find the port for each bus servo adapter, run this script:
```bash
python -m lerobot.find_port
lerobot-find-port
```
<hfoptions id="example">
@@ -116,7 +116,7 @@ The instructions for configuring the motors can be found in the SO101 [docs](./s
You can run this command to setup motors for LeKiwi. It will first setup the motors for arm (id 6..1) and then setup motors for wheels (9,8,7)
```bash
python -m lerobot.setup_motors \
lerobot-setup-motors \
--robot.type=lekiwi \
--robot.port=/dev/tty.usbmodem58760431551 # <- paste here the port found at previous step
```
@@ -174,7 +174,7 @@ The calibration process is very important because it allows a neural network tra
Make sure the arm is connected to the Raspberry Pi and run this script or API example (on the Raspberry Pi via SSH) to launch calibration of the follower arm:
```bash
python -m lerobot.calibrate \
lerobot-calibrate \
--robot.type=lekiwi \
--robot.id=my_awesome_kiwi # <- Give the robot a unique name
```
@@ -193,7 +193,7 @@ Then, to calibrate the leader arm (which is attached to the laptop/pc). Run the
<hfoption id="Command">
```bash
python -m lerobot.calibrate \
lerobot-calibrate \
--teleop.type=so100_leader \
--teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
--teleop.id=my_awesome_leader_arm # <- Give the robot a unique name

6
docs/source/smolvla.mdx
View File

@@ -54,7 +54,7 @@ If you don't have a gpu device, you can train using our notebook on [![Google Co
Pass your dataset to the training script using `--dataset.repo_id`. If you want to test your installation, run the following command where we use one of the datasets we collected for the [SmolVLA Paper](https://huggingface.co/papers/2506.01844).
```bash
cd lerobot && python -m lerobot.scripts.train \
cd lerobot && lerobot-train \
--policy.path=lerobot/smolvla_base \
--dataset.repo_id=${HF_USER}/mydataset \
--batch_size=64 \
@@ -73,7 +73,7 @@ cd lerobot && python -m lerobot.scripts.train \
Fine-tuning is an art. For a complete overview of the options for finetuning, run
```bash
python -m lerobot.scripts.train --help
lerobot-train --help
```
<p align="center">
@@ -97,7 +97,7 @@ Similarly for when recording an episode, it is recommended that you are logged i
Once you are logged in, you can run inference in your setup by doing:
```bash
python -m lerobot.record \
lerobot-record \
--robot.type=so101_follower \
--robot.port=/dev/ttyACM0 \ # <- Use your port
--robot.id=my_blue_follower_arm \ # <- Use your robot id

10
docs/source/so100.mdx
View File

@@ -26,7 +26,7 @@ Unlike the SO-101, the motor connectors are not easily accessible once the arm i
To find the port for each bus servo adapter, run this script:
```bash
python -m lerobot.find_port
lerobot-find-port
```
<hfoptions id="example">
@@ -93,7 +93,7 @@ For a visual reference on how to set the motor ids please refer to [this video](
<hfoption id="Command">
```bash
python -m lerobot.setup_motors \
lerobot-setup-motors \
--robot.type=so100_follower \
--robot.port=/dev/tty.usbmodem585A0076841 # <- paste here the port found at previous step
```
@@ -168,7 +168,7 @@ Do the same steps for the leader arm.
<hfoptions id="setup_motors">
<hfoption id="Command">
```bash
python -m lerobot.setup_motors \
lerobot-setup-motors \
--teleop.type=so100_leader \
--teleop.port=/dev/tty.usbmodem575E0031751 # <- paste here the port found at previous step
```
@@ -568,7 +568,7 @@ Run the following command or API example to calibrate the follower arm:
<hfoption id="Command">
```bash
python -m lerobot.calibrate \
lerobot-calibrate \
--robot.type=so100_follower \
--robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
--robot.id=my_awesome_follower_arm # <- Give the robot a unique name
@@ -606,7 +606,7 @@ Do the same steps to calibrate the leader arm, run the following command or API
<hfoption id="Command">
```bash
python -m lerobot.calibrate \
lerobot-calibrate \
--teleop.type=so100_leader \
--teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
--teleop.id=my_awesome_leader_arm # <- Give the robot a unique name

10
docs/source/so101.mdx
View File

@@ -162,7 +162,7 @@ It is advisable to install one 3-pin cable in the motor after placing them befor
To find the port for each bus servo adapter, connect MotorBus to your computer via USB and power. Run the following script and disconnect the MotorBus when prompted:
```bash
python -m lerobot.find_port
lerobot-find-port
```
<hfoptions id="example">
@@ -240,7 +240,7 @@ Connect the usb cable from your computer and the power supply to the follower ar
<hfoption id="Command">
```bash
python -m lerobot.setup_motors \
lerobot-setup-motors \
--robot.type=so101_follower \
--robot.port=/dev/tty.usbmodem585A0076841 # <- paste here the port found at previous step
```
@@ -316,7 +316,7 @@ Do the same steps for the leader arm.
<hfoption id="Command">
```bash
python -m lerobot.setup_motors \
lerobot-setup-motors \
--teleop.type=so101_leader \
--teleop.port=/dev/tty.usbmodem575E0031751 # <- paste here the port found at previous step
```
@@ -353,7 +353,7 @@ Run the following command or API example to calibrate the follower arm:
<hfoption id="Command">
```bash
python -m lerobot.calibrate \
lerobot-calibrate \
--robot.type=so101_follower \
--robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
--robot.id=my_awesome_follower_arm # <- Give the robot a unique name
@@ -402,7 +402,7 @@ Do the same steps to calibrate the leader arm, run the following command or API
<hfoption id="Command">
```bash
python -m lerobot.calibrate \
lerobot-calibrate \
--teleop.type=so101_leader \
--teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
--teleop.id=my_awesome_leader_arm # <- Give the robot a unique name

28
examples/4_train_policy_with_script.md
View File

@@ -62,7 +62,7 @@ By default, every field takes its default value specified in the dataclass. If a
Let's say that we want to train [Diffusion Policy](../src/lerobot/policies/diffusion) on the [pusht](https://huggingface.co/datasets/lerobot/pusht) dataset, using the [gym_pusht](https://github.com/huggingface/gym-pusht) environment for evaluation. The command to do so would look like this:
```bash
python -m lerobot.scripts.train \
lerobot-train \
--dataset.repo_id=lerobot/pusht \
--policy.type=diffusion \
--env.type=pusht
@@ -77,7 +77,7 @@ Let's break this down:
Let's see another example. Let's say you've been training [ACT](../src/lerobot/policies/act) on [lerobot/aloha_sim_insertion_human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human) using the [gym-aloha](https://github.com/huggingface/gym-aloha) environment for evaluation with:
```bash
python -m lerobot.scripts.train \
lerobot-train \
--policy.type=act \
--dataset.repo_id=lerobot/aloha_sim_insertion_human \
--env.type=aloha \
@@ -90,7 +90,7 @@ We now want to train a different policy for aloha on another task. We'll change
Looking at the [`AlohaEnv`](../src/lerobot/envs/configs.py) config, the task is `"AlohaInsertion-v0"` by default, which corresponds to the task we trained on in the command above. The [gym-aloha](https://github.com/huggingface/gym-aloha?tab=readme-ov-file#description) environment also has the `AlohaTransferCube-v0` task which corresponds to this other task we want to train on. Putting this together, we can train this new policy on this different task using:
```bash
python -m lerobot.scripts.train \
lerobot-train \
--policy.type=act \
--dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
--env.type=aloha \
@@ -127,7 +127,7 @@ Now, let's assume that we want to reproduce the run just above. That run has pro
We can then simply load the config values from this file using:
```bash
python -m lerobot.scripts.train \
lerobot-train \
--config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \
--output_dir=outputs/train/act_aloha_transfer_2
```
@@ -137,7 +137,7 @@ python -m lerobot.scripts.train \
Similarly to Hydra, we can still override some parameters in the CLI if we want to, e.g.:
```bash
python -m lerobot.scripts.train \
lerobot-train \
--config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \
--output_dir=outputs/train/act_aloha_transfer_2
--policy.n_action_steps=80
@@ -148,7 +148,7 @@ python -m lerobot.scripts.train \
`--config_path` can also accept the repo_id of a repo on the hub that contains a `train_config.json` file, e.g. running:
```bash
python -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
lerobot-train --config_path=lerobot/diffusion_pusht
```
will start a training run with the same configuration used for training [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht)
@@ -160,7 +160,7 @@ Being able to resume a training run is important in case it crashed or aborted f
Let's reuse the command from the previous run and add a few more options:
```bash
python -m lerobot.scripts.train \
lerobot-train \
--policy.type=act \
--dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
--env.type=aloha \
@@ -179,7 +179,7 @@ INFO 2025-01-24 16:10:56 ts/train.py:263 Checkpoint policy after step 100
Now let's simulate a crash by killing the process (hit `ctrl`+`c`). We can then simply resume this run from the last checkpoint available with:
```bash
python -m lerobot.scripts.train \
lerobot-train \
--config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \
--resume=true
```
@@ -190,7 +190,7 @@ Another reason for which you might want to resume a run is simply to extend trai
You could double the number of steps of the previous run with:
```bash
python -m lerobot.scripts.train \
lerobot-train \
--config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \
--resume=true \
--steps=200000
@@ -224,7 +224,7 @@ In addition to the features currently in Draccus, we've added a special `.path`
For example, we could fine-tune a [policy pre-trained on the aloha transfer task](https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human) on the aloha insertion task. We can achieve this with:
```bash
python -m lerobot.scripts.train \
lerobot-train \
--policy.path=lerobot/act_aloha_sim_transfer_cube_human \
--dataset.repo_id=lerobot/aloha_sim_insertion_human \
--env.type=aloha \
@@ -270,7 +270,7 @@ We'll summarize here the main use cases to remember from this tutorial.
#### Train a policy from scratch – CLI
```bash
python -m lerobot.scripts.train \
lerobot-train \
--policy.type=act \ # <- select 'act' policy
--env.type=pusht \ # <- select 'pusht' environment
--dataset.repo_id=lerobot/pusht # <- train on this dataset
@@ -279,7 +279,7 @@ python -m lerobot.scripts.train \
#### Train a policy from scratch - config file + CLI
```bash
python -m lerobot.scripts.train \
lerobot-train \
--config_path=path/to/pretrained_model \ # <- can also be a repo_id
--policy.n_action_steps=80 # <- you may still override values
```
@@ -287,7 +287,7 @@ python -m lerobot.scripts.train \
#### Resume/continue a training run
```bash
python -m lerobot.scripts.train \
lerobot-train \
--config_path=checkpoint/pretrained_model/ \
--resume=true \
--steps=200000 # <- you can change some training parameters
@@ -296,7 +296,7 @@ python -m lerobot.scripts.train \
#### Fine-tuning
```bash
python -m lerobot.scripts.train \
lerobot-train \
--policy.path=lerobot/act_aloha_sim_transfer_cube_human \ # <- can also be a local path to a checkpoint
--dataset.repo_id=lerobot/aloha_sim_insertion_human \
--env.type=aloha \

2
examples/backward_compatibility/replay.py
View File

@@ -18,7 +18,7 @@ Replays the actions of an episode from a dataset on a robot.
Example:
```shell
python -m lerobot.replay \
lerobot-replay \
--robot.type=so100_follower \
--robot.port=/dev/tty.usbmodem58760431541 \
--robot.id=black \

2
src/lerobot/calibrate.py
View File

@@ -18,7 +18,7 @@ Helper to recalibrate your device (robot or teleoperator).
Example:
```shell
python -m lerobot.calibrate \
lerobot-calibrate \
--teleop.type=so100_leader \
--teleop.port=/dev/tty.usbmodem58760431551 \
--teleop.id=blue

5
src/lerobot/cameras/opencv/camera_opencv.py
View File

@@ -60,7 +60,7 @@ class OpenCVCamera(Camera):
or port changes, especially on Linux. Use the provided utility script to find
available camera indices or paths:
```bash
python -m lerobot.find_cameras opencv
lerobot-find-cameras opencv
```
The camera's default settings (FPS, resolution, color mode) are used unless
@@ -165,8 +165,7 @@ class OpenCVCamera(Camera):
self.videocapture.release()
self.videocapture = None
raise ConnectionError(
f"Failed to open {self}."
f"Run `python -m lerobot.find_cameras opencv` to find available cameras."
f"Failed to open {self}.Run `lerobot-find-cameras opencv` to find available cameras."
)
self._configure_capture_settings()

5
src/lerobot/cameras/realsense/camera_realsense.py
View File

@@ -51,7 +51,7 @@ class RealSenseCamera(Camera):
Use the provided utility script to find available camera indices and default profiles:
```bash
python -m lerobot.find_cameras realsense
lerobot-find-cameras realsense
```
A `RealSenseCamera` instance requires a configuration object specifying the
@@ -176,8 +176,7 @@ class RealSenseCamera(Camera):
self.rs_profile = None
self.rs_pipeline = None
raise ConnectionError(
f"Failed to open {self}."
"Run `python -m lerobot.find_cameras realsense` to find available cameras."
f"Failed to open {self}.Run `lerobot-find-cameras realsense` to find available cameras."
) from e
self._configure_capture_settings()

2
src/lerobot/find_cameras.py
View File

@@ -20,7 +20,7 @@ Helper to find the camera devices available in your system.
Example:
```shell
python -m lerobot.find_cameras
lerobot-find-cameras
```
"""

2
src/lerobot/find_port.py
View File

@@ -18,7 +18,7 @@ Helper to find the USB port associated with your MotorsBus.
Example:
```shell
python -m lerobot.find_port
lerobot-find-port
```
"""

4
src/lerobot/motors/motors_bus.py
View File

@@ -222,7 +222,7 @@ class MotorsBus(abc.ABC):
A MotorsBus subclass instance requires a port (e.g. `FeetechMotorsBus(port="/dev/tty.usbmodem575E0031751"`)).
To find the port, you can run our utility script:
```bash
python -m lerobot.find_port.py
lerobot-find-port.py
>>> Finding all available ports for the MotorsBus.
>>> ["/dev/tty.usbmodem575E0032081", "/dev/tty.usbmodem575E0031751"]
>>> Remove the usb cable from your MotorsBus and press Enter when done.
@@ -446,7 +446,7 @@ class MotorsBus(abc.ABC):
except (FileNotFoundError, OSError, serial.SerialException) as e:
raise ConnectionError(
f"\nCould not connect on port '{self.port}'. Make sure you are using the correct port."
"\nTry running `python -m lerobot.find_port`\n"
"\nTry running `lerobot-find-port`\n"
) from e
@abc.abstractmethod

4
src/lerobot/policies/pi0/modeling_pi0.py
View File

@@ -30,7 +30,7 @@ pip install -e ".[pi0]"
Example of finetuning the pi0 pretrained model (`pi0_base` in `openpi`):
```bash
python -m lerobot.scripts.train \
lerobot-train \
--policy.path=lerobot/pi0 \
--dataset.repo_id=danaaubakirova/koch_test
```
@@ -38,7 +38,7 @@ python -m lerobot.scripts.train \
Example of finetuning the pi0 neural network with PaliGemma and expert Gemma
pretrained with VLM default parameters before pi0 finetuning:
```bash
python -m lerobot.scripts.train \
lerobot-train \
--policy.type=pi0 \
--dataset.repo_id=danaaubakirova/koch_test
```

4
src/lerobot/policies/pi0fast/modeling_pi0fast.py
View File

@@ -25,14 +25,14 @@ Disclaimer: It is not expected to perform as well as the original implementation
Example of finetuning the pi0+FAST pretrained model (`pi0_fast_base` in `openpi`):
```bash
python -m lerobot.scripts.train \
lerobot-train \
--policy.path=lerobot/pi0fast_base \
--dataset.repo_id=danaaubakirova/koch_test
```
Example of training the pi0+FAST neural network with from scratch:
```bash
python -m lerobot.scripts.train \
lerobot-train \
--policy.type=pi0fast \
--dataset.repo_id=danaaubakirova/koch_test
```

4
src/lerobot/policies/smolvla/modeling_smolvla.py
View File

@@ -28,7 +28,7 @@ pip install -e ".[smolvla]"
Example of finetuning the smolvla pretrained model (`smolvla_base`):
```bash
python -m lerobot.scripts.train \
lerobot-train \
--policy.path=lerobot/smolvla_base \
--dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \
--batch_size=64 \
@@ -38,7 +38,7 @@ python -m lerobot.scripts.train \
Example of finetuning a smolVLA. SmolVLA is composed of a pretrained VLM,
and an action expert.
```bash
python -m lerobot.scripts.train \
lerobot-train \
--policy.type=smolvla \
--dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \
--batch_size=64 \

4
src/lerobot/record.py
View File

@@ -18,7 +18,7 @@ Records a dataset. Actions for the robot can be either generated by teleoperatio
Example:
```shell
python -m lerobot.record \
lerobot-record \
--robot.type=so100_follower \
--robot.port=/dev/tty.usbmodem58760431541 \
--robot.cameras="{laptop: {type: opencv, camera_index: 0, width: 640, height: 480}}" \
@@ -36,7 +36,7 @@ python -m lerobot.record \
Example recording with bimanual so100:
```shell
python -m lerobot.record \
lerobot-record \
--robot.type=bi_so100_follower \
--robot.left_arm_port=/dev/tty.usbmodem5A460851411 \
--robot.right_arm_port=/dev/tty.usbmodem5A460812391 \

4
src/lerobot/replay.py
View File

@@ -18,7 +18,7 @@ Replays the actions of an episode from a dataset on a robot.
Examples:
```shell
python -m lerobot.replay \
lerobot-replay \
--robot.type=so100_follower \
--robot.port=/dev/tty.usbmodem58760431541 \
--robot.id=black \
@@ -28,7 +28,7 @@ python -m lerobot.replay \
Example replay with bimanual so100:
```shell
python -m lerobot.replay \
lerobot-replay \
--robot.type=bi_so100_follower \
--robot.left_arm_port=/dev/tty.usbmodem5A460851411 \
--robot.right_arm_port=/dev/tty.usbmodem5A460812391 \

4
src/lerobot/robots/viperx/README.md
View File

@@ -141,10 +141,10 @@ python lerobot/scripts/control_robot.py \
## Train a policy
To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
To train a policy to control your robot, use the [`lerobot-train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
```bash
python -m lerobot.scripts.train \
lerobot-train \
--dataset.repo_id=${HF_USER}/aloha_test \
--policy.type=act \
--output_dir=outputs/train/act_aloha_test \

4
src/lerobot/scripts/eval.py
View File

@@ -21,7 +21,7 @@ You want to evaluate a model from the hub (eg: https://huggingface.co/lerobot/di
for 10 episodes.
```
python -m lerobot.scripts.eval \
lerobot-eval \
--policy.path=lerobot/diffusion_pusht \
--env.type=pusht \
--eval.batch_size=10 \
@@ -32,7 +32,7 @@ python -m lerobot.scripts.eval \
OR, you want to evaluate a model checkpoint from the LeRobot training script for 10 episodes.
```
python -m lerobot.scripts.eval \
lerobot-eval \
--policy.path=outputs/train/diffusion_pusht/checkpoints/005000/pretrained_model \
--env.type=pusht \
--eval.batch_size=10 \

2
src/lerobot/setup_motors.py
View File

@@ -18,7 +18,7 @@ Helper to set motor ids and baudrate.
Example:
```shell
python -m lerobot.setup_motors \
lerobot-setup-motors \
--teleop.type=so100_leader \
--teleop.port=/dev/tty.usbmodem575E0031751
```

4
src/lerobot/teleoperate.py
View File

@@ -18,7 +18,7 @@ Simple script to control a robot from teleoperation.
Example:
```shell
python -m lerobot.teleoperate \
lerobot-teleoperate \
--robot.type=so101_follower \
--robot.port=/dev/tty.usbmodem58760431541 \
--robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
@@ -32,7 +32,7 @@ python -m lerobot.teleoperate \
Example teleoperation with bimanual so100:
```shell
python -m lerobot.teleoperate \
lerobot-teleoperate \
--robot.type=bi_so100_follower \
--robot.left_arm_port=/dev/tty.usbmodem5A460851411 \
--robot.right_arm_port=/dev/tty.usbmodem5A460812391 \

4
src/lerobot/templates/lerobot_modelcard_template.md
View File

@@ -44,7 +44,7 @@ Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
@@ -59,7 +59,7 @@ _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \