AM-ARM200 — Full Workflow

← 教程资料库 · AlohaMini 硬件与操作

适用型号请结合本页硬件配置确认。

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Prerequisites: complete install.md first.
Hardware profiles: see profiles.md.

Single-arm setup — one PC, no Raspberry Pi required.

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1. Port Configuration

Plug the leader in first, run the finder, note the port. Then plug in the follower and note its port:

lerobot-find-port
# or check directly:
ls /dev/ttyACM*

Port numbers can change after reconnecting or rebooting. Note each path before moving to the next device.

2. Camera Configuration

lerobot-find-cameras

Note each camera index. Use one USB port per camera — do not share a USB hub between multiple cameras.

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3. Calibration

Calibrate the leader

lerobot-calibrate \
  --teleop.type=so101_leader \
  --teleop.port=/dev/ttyACM0 \
  --teleop.id=my_leader \
  --teleop.arm_profile=am-leader-6dof

Calibrate the follower

lerobot-calibrate \
  --robot.type=so101_follower \
  --robot.port=/dev/ttyACM1 \
  --robot.id=my_follower \
  --robot.arm_profile=am-follower-6dof

AM-ARM200 Pro: replace am-follower-6dof with am-follower-6dof-hd.

Power-cycle both arms after calibration for changes to take effect.

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4. Teleoperation

lerobot-teleoperate \
  --teleop.type=so101_leader \
  --teleop.port=/dev/ttyACM0 \
  --teleop.id=my_leader \
  --teleop.arm_profile=am-leader-6dof \
  --robot.type=so101_follower \
  --robot.port=/dev/ttyACM1 \
  --robot.id=my_follower \
  --robot.arm_profile=am-follower-6dof

Move the leader by hand — the follower mirrors it in real time.

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5. Dataset Recording

Make sure both arms are connected and calibrated before recording. Camera indices come from lerobot-find-cameras (see §2).

Create new dataset:

lerobot-record \
  --robot.type=so101_follower \
  --robot.port=/dev/ttyACM1 \
  --robot.id=my_follower \
  --robot.arm_profile=am-follower-6dof \
  --robot.cameras="{cam_wrist: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}, cam_top: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30}}" \
  --teleop.type=so101_leader \
  --teleop.port=/dev/ttyACM0 \
  --teleop.id=my_leader \
  --teleop.arm_profile=am-leader-6dof \
  --dataset.repo_id=$HF_USER/am_arm_test \
  --dataset.num_episodes=50 \
  --dataset.fps=30 \
  --dataset.episode_time_s=45 \
  --dataset.reset_time_s=8 \
  --dataset.single_task "pickup1" \
  --display_data=true

Resume existing dataset — --dataset.root specifies a local working directory; LeRobot downloads the existing dataset metadata from Hub automatically if the path doesn't exist yet:

lerobot-record \
  --robot.type=so101_follower \
  --robot.port=/dev/ttyACM1 \
  --robot.id=my_follower \
  --robot.arm_profile=am-follower-6dof \
  --robot.cameras="{cam_wrist: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}, cam_top: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30}}" \
  --teleop.type=so101_leader \
  --teleop.port=/dev/ttyACM0 \
  --teleop.id=my_leader \
  --teleop.arm_profile=am-leader-6dof \
  --dataset.repo_id=$HF_USER/am_arm_test \
  --dataset.root=$HOME/lerobot_datasets/$HF_USER/am_arm_test \
  --dataset.num_episodes=50 \
  --dataset.fps=30 \
  --dataset.episode_time_s=45 \
  --dataset.reset_time_s=8 \
  --dataset.single_task "pickup1" \
  --display_data=true \
  --resume=true

AM-ARM200 Pro: replace am-follower-6dof with am-follower-6dof-hd.
Camera names (cam_wrist, cam_top) are arbitrary but become dataset field names — keep them consistent across all recording sessions.
To use a single camera, remove the cam_top entry.

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6. Dataset Replay

Replay a recorded episode on the follower arm to verify the data:

lerobot-replay \
  --robot.type=so101_follower \
  --robot.port=/dev/ttyACM1 \
  --robot.id=my_follower \
  --robot.arm_profile=am-follower-6dof \
  --dataset.repo_id=$HF_USER/am_arm_test \
  --dataset.episode=0

AM-ARM200 Pro: replace am-follower-6dof with am-follower-6dof-hd.

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7. Dataset Visualization

lerobot-dataset-viz \
  --repo-id $HF_USER/am_arm_test \
  --episode-index 0 \
  --display-compressed-images

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8. Training

Local training

lerobot-train \
  --dataset.repo_id=$HF_USER/am_arm_test \
  --policy.type=act \
  --output_dir=outputs/train/act_your_dataset1 \
  --job_name=act_your_dataset \
  --policy.device=cuda \
  --wandb.enable=false \
  --policy.repo_id=$HF_USER/act_policy \
  --dataset.video_backend=pyav

No local GPU?

Use any cloud GPU provider (e.g. AutoDL, Lambda Labs, Vast.ai). Set up the environment the same way as local, run the same training command, then copy the checkpoint back to your machine for evaluation.

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9. Evaluation

Copy the trained model to your local machine, then run inference on the follower arm:

lerobot-rollout \
  --strategy.type=base \
  --robot.type=so101_follower \
  --robot.port=/dev/ttyACM1 \
  --robot.id=my_follower \
  --robot.arm_profile=am-follower-6dof \
  --policy.path=outputs/train/act_your_dataset1/checkpoints/020000/pretrained_model \
  --task="your task description"

AM-ARM200 Pro: replace am-follower-6dof with am-follower-6dof-hd.

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10. Debug

See Debug Command Summary for the full list of debugging utilities.