forked from tangger/lerobot
Remove unecessary id
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@@ -90,8 +90,7 @@ Connect the usb cable from your computer and the 5V power supply to the leader a
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```bash
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python -m lerobot.setup_motors \
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--device.type=so100_leader \
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--device.port=/dev/tty.usbmodem575E0031751 \ # <- paste here the port found at previous step
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--device.id=my_awesome_leader_arm # <- give it a nice, unique name
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--device.port=/dev/tty.usbmodem575E0031751 # <- paste here the port found at previous step
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```
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Note that the command above is equivalent to running the following script:
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@@ -103,7 +102,6 @@ Note that the command above is equivalent to running the following script:
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config = KochLeaderConfig(
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port="/dev/tty.usbmodem575E0031751",
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id="my_awesome_leader_arm",
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)
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leader = KochLeader(config)
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leader.setup_motors()
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@@ -319,7 +317,7 @@ python lerobot/scripts/control_robot.py \
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```
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As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
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1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with (e.g. `outputs/train/eval_act_so100_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_so100_test`).
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1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with (e.g. `outputs/train/eval_act_so100_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_so100_test`).
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2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_so100_test`).
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## K. More Information
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