File	Description
`policy.py`	ACT inference wrapper (`BasePolicyServer` subclass)
`server.py`	HTTP policy server (challenge protocol)
`Dockerfile`	CPU-only build (`lerobot==0.4.3`, `torch` CPU)
`pretrained_model/`	Trained ACT policy (LeRobot v3 format)
`dataset/meta/`	LeRobot dataset metadata (for `LeRobotDatasetMetadata`)
`constraints.txt`	Pinned versions

Usage

bash

docker build -t lehome-act-80k .
docker run --rm -p 8080:8080 lehome-act-80k

Then evaluate with the official challenge code:

bash

python -m scripts.eval --policy_type docker \
  --garment_type top_long --headless --device cpu --enable_cameras

Model Details

Architecture: ACT (Action Chunking Transformer)
Action / state dim: 12
Image inputs: top_rgb, left_rgb, right_rgb (480×640×3)
Training: 80K steps, batch size 16, fine-tuned from act_four_types/100K
Dataset: four_types_pant_boosted (1500 episodes, 397K frames, 4 garment categories with pant emphasis)