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Fix typos (#1070)

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omahs
2025-05-05 10:35:32 +02:00
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docs/source/assemble_so101.mdx
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@@ -194,7 +194,7 @@ Here is a video of the process:
</div>
### Clean Parts
Remove all support material from the 3D-printed parts, the easiest wat to do this is using a small screwdriver to get underneath the support material.
Remove all support material from the 3D-printed parts, the easiest way to do this is using a small screwdriver to get underneath the support material.
### Joint 1

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docs/source/getting_started_real_world_robot.mdx
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@@ -152,7 +152,7 @@ If everything is set up correctly, you can proceed with the rest of the tutorial
## Teleoperate with cameras
We can now teleoperate again while at the same time visualzing the camera's and joint positions with `rerun`.
We can now teleoperate again while at the same time visualizing the camera's and joint positions with `rerun`.
```bash
python lerobot/scripts/control_robot.py \
@@ -165,7 +165,7 @@ python lerobot/scripts/control_robot.py \
Once you're familiar with teleoperation, you can record your first dataset with SO-101.
We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you've can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens).
We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens).
Add your token to the cli by running this command:
```bash
@@ -318,7 +318,7 @@ python lerobot/scripts/train.py \
Let's explain the command:
1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/so101_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.
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 states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
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`.