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kunhsiang/
joint-inverse-act-libero-2026-03-29-185058-1k

Name: joint-inverse-act-libero-2026-03-29-185058-1k
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joint-inverse-act-libero-2026-03-29-185058-1k

備份自 ID Lab lerobot-vla-fbagent 專案，含完整中間 checkpoint。

訓練資訊

項目	值
Policy Type	`joint_inverse_act`
Dataset	`HuggingFaceVLA/libero`
Max Steps	1,000
Checkpoints	1 個
Seed	?
Batch Size	?
Learning Rate	?
Num Workers	?
Chunk Size	100
Git Commit	`d440da2`
Source Dir	`18-50-58_joint_inverse_act`

復現指令

bash

git clone --recurse-submodules git@github.com:KunHsiang/lerobot-vla-fbagent.git
cd lerobot-vla-fbagent
git checkout d440da2
uv sync

HF_HUB_ENABLE_HF_TRANSFER=1 .venv/bin/python scripts/train_with_fb.py \
    --policy.type=joint_inverse_act \
    --policy.repo_id=kunhsiang/joint-inverse-act-libero-2026-03-29-185058-1k \
    --dataset.repo_id=HuggingFaceVLA/libero \
    --training.seed=? \
    --training.batch_size=? \
    --training.learning_rate=? \
    --training.num_workers=?

Checkpoint 結構

checkpoints/
  001000/          # step 1,000
  ...
  001000/          # final (1,000 steps)

每個 step 目錄包含:

pretrained_model/model.safetensors — 模型權重
pretrained_model/config.json — 模型設定
pretrained_model/train_config.json — 完整訓練設定
training_state/optimizer_state.safetensors — Optimizer 狀態（可 resume）
training_state/rng_state.safetensors — RNG 狀態（可 resume）

Resume 訓練

bash

.venv/bin/python scripts/train_with_fb.py \
    --policy.type=joint_inverse_act \
    --resume=true \
    --policy.pretrained_path=checkpoints/001000/pretrained_model