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chore(docs): update instructions for change in device and use_amp (#843)

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4
README.md
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@ -232,8 +232,8 @@ python lerobot/scripts/eval.py \
--env.type=pusht \
--eval.batch_size=10 \
--eval.n_episodes=10 \
--use_amp=false \
--device=cuda
--policy.use_amp=false \
--policy.device=cuda
```
Note: After training your own policy, you can re-evaluate the checkpoints with:

12
examples/10_use_so100.md
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@ -454,8 +454,8 @@ Next, you'll need to calibrate your SO-100 robot to ensure that the leader and f
You will need to move the follower arm to these positions sequentially:
| 1. Zero position | 2. Rotated position | 3. Rest position |
|---|---|---|
| 1. Zero position | 2. Rotated position | 3. Rest position |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| <img src="../media/so100/follower_zero.webp?raw=true" alt="SO-100 follower arm zero position" title="SO-100 follower arm zero position" style="width:100%;"> | <img src="../media/so100/follower_rotated.webp?raw=true" alt="SO-100 follower arm rotated position" title="SO-100 follower arm rotated position" style="width:100%;"> | <img src="../media/so100/follower_rest.webp?raw=true" alt="SO-100 follower arm rest position" title="SO-100 follower arm rest position" style="width:100%;"> |
Make sure both arms are connected and run this script to launch manual calibration:
@ -470,8 +470,8 @@ python lerobot/scripts/control_robot.py \
#### b. Manual calibration of leader arm
Follow step 6 of the [assembly video](https://youtu.be/FioA2oeFZ5I?t=724) which illustrates the manual calibration. You will need to move the leader arm to these positions sequentially:
| 1. Zero position | 2. Rotated position | 3. Rest position |
|---|---|---|
| 1. Zero position | 2. Rotated position | 3. Rest position |
| ------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
| <img src="../media/so100/leader_zero.webp?raw=true" alt="SO-100 leader arm zero position" title="SO-100 leader arm zero position" style="width:100%;"> | <img src="../media/so100/leader_rotated.webp?raw=true" alt="SO-100 leader arm rotated position" title="SO-100 leader arm rotated position" style="width:100%;"> | <img src="../media/so100/leader_rest.webp?raw=true" alt="SO-100 leader arm rest position" title="SO-100 leader arm rest position" style="width:100%;"> |
Run this script to launch manual calibration:
@ -571,14 +571,14 @@ python lerobot/scripts/train.py \
--policy.type=act \
--output_dir=outputs/train/act_so100_test \
--job_name=act_so100_test \
--device=cuda \
--policy.device=cuda \
--wandb.enable=true
```
Let's explain it:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/so100_test`.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
4. We provided `device=cuda` since we are training on a Nvidia GPU, but you could use `device=mps` to train on Apple silicon.
4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
Training should take several hours. You will find checkpoints in `outputs/train/act_so100_test/checkpoints`.

40
examples/11_use_lekiwi.md
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@ -366,8 +366,8 @@ Now we have to calibrate the leader arm and the follower arm. The wheel motors d
You will need to move the follower arm to these positions sequentially:
| 1. Zero position | 2. Rotated position | 3. Rest position |
|---|---|---|
| 1. Zero position | 2. Rotated position | 3. Rest position |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <img src="../media/lekiwi/mobile_calib_zero.webp?raw=true" alt="SO-100 follower arm zero position" title="SO-100 follower arm zero position" style="width:100%;"> | <img src="../media/lekiwi/mobile_calib_rotated.webp?raw=true" alt="SO-100 follower arm rotated position" title="SO-100 follower arm rotated position" style="width:100%;"> | <img src="../media/lekiwi/mobile_calib_rest.webp?raw=true" alt="SO-100 follower arm rest position" title="SO-100 follower arm rest position" style="width:100%;"> |
Make sure the arm is connected to the Raspberry Pi and run this script (on the Raspberry Pi) to launch manual calibration:
@ -385,8 +385,8 @@ If you have the **wired** LeKiwi version please run all commands including this
### Calibrate leader arm
Then to calibrate the leader arm (which is attached to the laptop/pc). You will need to move the leader arm to these positions sequentially:
| 1. Zero position | 2. Rotated position | 3. Rest position |
|---|---|---|
| 1. Zero position | 2. Rotated position | 3. Rest position |
| ------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
| <img src="../media/so100/leader_zero.webp?raw=true" alt="SO-100 leader arm zero position" title="SO-100 leader arm zero position" style="width:100%;"> | <img src="../media/so100/leader_rotated.webp?raw=true" alt="SO-100 leader arm rotated position" title="SO-100 leader arm rotated position" style="width:100%;"> | <img src="../media/so100/leader_rest.webp?raw=true" alt="SO-100 leader arm rest position" title="SO-100 leader arm rest position" style="width:100%;"> |
Run this script (on your laptop/pc) to launch manual calibration:
@ -416,22 +416,22 @@ python lerobot/scripts/control_robot.py \
You should see on your laptop something like this: ```[INFO] Connected to remote robot at tcp://172.17.133.91:5555 and video stream at tcp://172.17.133.91:5556.``` Now you can move the leader arm and use the keyboard (w,a,s,d) to drive forward, left, backwards, right. And use (z,x) to turn left or turn right. You can use (r,f) to increase and decrease the speed of the mobile robot. There are three speed modes, see the table below:
| Speed Mode | Linear Speed (m/s) | Rotation Speed (deg/s) |
|------------|-------------------|-----------------------|
| Fast | 0.4 | 90 |
| Medium | 0.25 | 60 |
| Slow | 0.1 | 30 |
| ---------- | ------------------ | ---------------------- |
| Fast | 0.4 | 90 |
| Medium | 0.25 | 60 |
| Slow | 0.1 | 30 |
| Key | Action |
|------|--------------------------------|
| W | Move forward |
| A | Move left |
| S | Move backward |
| D | Move right |
| Z | Turn left |
| X | Turn right |
| R | Increase speed |
| F | Decrease speed |
| Key | Action |
| --- | -------------- |
| W | Move forward |
| A | Move left |
| S | Move backward |
| D | Move right |
| Z | Turn left |
| X | Turn right |
| R | Increase speed |
| F | Decrease speed |
> [!TIP]
> If you use a different keyboard you can change the keys for each command in the [`LeKiwiRobotConfig`](../lerobot/common/robot_devices/robots/configs.py).
@ -549,14 +549,14 @@ python lerobot/scripts/train.py \
--policy.type=act \
--output_dir=outputs/train/act_lekiwi_test \
--job_name=act_lekiwi_test \
--device=cuda \
--policy.device=cuda \
--wandb.enable=true
```
Let's explain it:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/lekiwi_test`.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
4. We provided `device=cuda` since we are training on a Nvidia GPU, but you could use `device=mps` to train on Apple silicon.
4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
Training should take several hours. You will find checkpoints in `outputs/train/act_lekiwi_test/checkpoints`.

12
examples/11_use_moss.md
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@ -176,8 +176,8 @@ Next, you'll need to calibrate your Moss v1 robot to ensure that the leader and
You will need to move the follower arm to these positions sequentially:
| 1. Zero position | 2. Rotated position | 3. Rest position |
|---|---|---|
| 1. Zero position | 2. Rotated position | 3. Rest position |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <img src="../media/moss/follower_zero.webp?raw=true" alt="Moss v1 follower arm zero position" title="Moss v1 follower arm zero position" style="width:100%;"> | <img src="../media/moss/follower_rotated.webp?raw=true" alt="Moss v1 follower arm rotated position" title="Moss v1 follower arm rotated position" style="width:100%;"> | <img src="../media/moss/follower_rest.webp?raw=true" alt="Moss v1 follower arm rest position" title="Moss v1 follower arm rest position" style="width:100%;"> |
Make sure both arms are connected and run this script to launch manual calibration:
@ -192,8 +192,8 @@ python lerobot/scripts/control_robot.py \
**Manual calibration of leader arm**
Follow step 6 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) which illustrates the manual calibration. You will need to move the leader arm to these positions sequentially:
| 1. Zero position | 2. Rotated position | 3. Rest position |
|---|---|---|
| 1. Zero position | 2. Rotated position | 3. Rest position |
| ------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <img src="../media/moss/leader_zero.webp?raw=true" alt="Moss v1 leader arm zero position" title="Moss v1 leader arm zero position" style="width:100%;"> | <img src="../media/moss/leader_rotated.webp?raw=true" alt="Moss v1 leader arm rotated position" title="Moss v1 leader arm rotated position" style="width:100%;"> | <img src="../media/moss/leader_rest.webp?raw=true" alt="Moss v1 leader arm rest position" title="Moss v1 leader arm rest position" style="width:100%;"> |
Run this script to launch manual calibration:
@ -293,14 +293,14 @@ python lerobot/scripts/train.py \
--policy.type=act \
--output_dir=outputs/train/act_moss_test \
--job_name=act_moss_test \
--device=cuda \
--policy.device=cuda \
--wandb.enable=true
```
Let's explain it:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/moss_test`.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
4. We provided `device=cuda` since we are training on a Nvidia GPU, but you could use `device=mps` to train on Apple silicon.
4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
Training should take several hours. You will find checkpoints in `outputs/train/act_moss_test/checkpoints`.

2
examples/4_train_policy_with_script.md
View File

@ -1,5 +1,5 @@
This tutorial will explain the training script, how to use it, and particularly how to configure everything needed for the training run.
> **Note:** The following assume you're running these commands on a machine equipped with a cuda GPU. If you don't have one (or if you're using a Mac), you can add `--device=cpu` (`--device=mps` respectively). However, be advised that the code executes much slower on cpu.
> **Note:** The following assume you're running these commands on a machine equipped with a cuda GPU. If you don't have one (or if you're using a Mac), you can add `--policy.device=cpu` (`--policy.device=mps` respectively). However, be advised that the code executes much slower on cpu.
## The training script

12
examples/7_get_started_with_real_robot.md
View File

@ -386,14 +386,14 @@ When you connect your robot for the first time, the [`ManipulatorRobot`](../lero
Here are the positions you'll move the follower arm to:
| 1. Zero position | 2. Rotated position | 3. Rest position |
|---|---|---|
| 1. Zero position | 2. Rotated position | 3. Rest position |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <img src="../media/koch/follower_zero.webp?raw=true" alt="Koch v1.1 follower arm zero position" title="Koch v1.1 follower arm zero position" style="width:100%;"> | <img src="../media/koch/follower_rotated.webp?raw=true" alt="Koch v1.1 follower arm rotated position" title="Koch v1.1 follower arm rotated position" style="width:100%;"> | <img src="../media/koch/follower_rest.webp?raw=true" alt="Koch v1.1 follower arm rest position" title="Koch v1.1 follower arm rest position" style="width:100%;"> |
And here are the corresponding positions for the leader arm:
| 1. Zero position | 2. Rotated position | 3. Rest position |
|---|---|---|
| 1. Zero position | 2. Rotated position | 3. Rest position |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <img src="../media/koch/leader_zero.webp?raw=true" alt="Koch v1.1 leader arm zero position" title="Koch v1.1 leader arm zero position" style="width:100%;"> | <img src="../media/koch/leader_rotated.webp?raw=true" alt="Koch v1.1 leader arm rotated position" title="Koch v1.1 leader arm rotated position" style="width:100%;"> | <img src="../media/koch/leader_rest.webp?raw=true" alt="Koch v1.1 leader arm rest position" title="Koch v1.1 leader arm rest position" style="width:100%;"> |
You can watch a [video tutorial of the calibration procedure](https://youtu.be/8drnU9uRY24) for more details.
@ -898,14 +898,14 @@ python lerobot/scripts/train.py \
--policy.type=act \
--output_dir=outputs/train/act_koch_test \
--job_name=act_koch_test \
--device=cuda \
--policy.device=cuda \
--wandb.enable=true
```
Let's explain it:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/koch_test`.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
4. We provided `device=cuda` since we are training on a Nvidia GPU, but you could use `device=mps` to train on Apple silicon.
4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
For more information on the `train` script see the previous tutorial: [`examples/4_train_policy_with_script.md`](../examples/4_train_policy_with_script.md)

4
examples/9_use_aloha.md
View File

@ -135,14 +135,14 @@ python lerobot/scripts/train.py \
--policy.type=act \
--output_dir=outputs/train/act_aloha_test \
--job_name=act_aloha_test \
--device=cuda \
--policy.device=cuda \
--wandb.enable=true
```
Let's explain it:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/aloha_test`.
2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
4. We provided `device=cuda` since we are training on a Nvidia GPU, but you could use `device=mps` to train on Apple silicon.
4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
For more information on the `train` script see the previous tutorial: [`examples/4_train_policy_with_script.md`](../examples/4_train_policy_with_script.md)