Developer shares key lessons from training an AI policy on Hugging Face's SO-ARM101

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A developer documented their experience building and training an Action Chunking with Transformer (ACT) policy using Hugging Face's LeRobot ecosystem and the low-cost, 3D-printed SO-ARM101 robotic arm. The project ran on a MacBook using a pyenv and miniconda3 environment, with the LeRobot package installed via PyPI. A multi-camera setup — including a wrist camera for close-up feedback and an overhead camera for global context — was recommended for better policy consistency over basic single-camera configurations. The developer advised recording datasets at lower resolutions like 640x480 rather than full 1080p to significantly reduce dataset size, speed up training, and cut memory usage. Allocating at least two encoder threads per camera during episode recording was also flagged as essential to prevent frame drops during teleoperation.

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