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fad6045 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | #!/bin/bash
# Fallback if SmolVLA does not learn the baseline: train ACT from scratch (ResNet-18
# image backbone, pretrained on ImageNet) on the same data, overhead and wrist cameras,
# and push it to the Hugging Face Hub. ACT reads the camera keys as they are, so no
# rename map. Run from the project root on the GPU pod:
# ROOT=data/so101_chess_baseline REPO=Machanize/chess_phase_act_baseline bash sim/train_act.sh
set -euo pipefail
cd "$(dirname "$0")/.."
ROOT=${ROOT:-data/so101_chess_baseline}
REPO=${REPO:-Machanize/chess_phase_act_baseline}
JOB=${JOB:-chess_phase_act_baseline}
.venv/bin/lerobot-train \
--policy.type=act \
--dataset.repo_id="${DATASET_ID:-local/so101_chess_baseline}" \
--dataset.root="$ROOT" \
--batch_size="${BATCH:-32}" \
--steps="${STEPS:-40000}" \
--save_freq="${SAVE_FREQ:-10000}" \
--log_freq=100 \
--num_workers="${WORKERS:-16}" \
--output_dir="${OUT:-outputs/$JOB}" \
--job_name="$JOB" \
--policy.device=cuda \
--wandb.enable=false \
--policy.push_to_hub="${PUSH:-true}" \
--policy.repo_id="$REPO" \
--policy.private=false
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