| --- |
| license: other |
| library_name: pytorch |
| tags: |
| - robotics |
| - libero |
| - imitation-learning |
| - world-action-model |
| - pfd |
| - privileged-foresight-distillation |
| --- |
| |
| # PFD LIBERO 12x12 Checkpoint |
|
|
| This repository contains the LIBERO 12x12 PFD checkpoint for **Privileged Foresight Distillation: Zero-Cost Future Correction for World Action Models**. |
|
|
| - Code: https://github.com/PengchengFang-cs/PFD |
| - Paper: https://arxiv.org/abs/2604.25859 |
| - Checkpoint: `libero_pfd_action512_partial_12x12_step62000.pt` |
| - Model config: `config.yaml` |
| - Dataset normalization stats: `dataset_stats.json` |
|
|
| The Python package in the code release is still named `fastwam` for compatibility with the original training and evaluation paths. |
|
|
| ## Model |
|
|
| - Task: `libero_uncond_2cam224_1e-4` |
| - Model config: `fastwam_pfd_action512_partial` |
| - PFD stage: `s1` |
| - PFD training mode: `action512_partial` |
| - Partial trainable depth: action last 12 layers, video last 12 layers |
| - Base initialization: `libero_uncond_2cam224.pt` |
| - Training batch size: 32 |
| - Training epochs: 30 |
| - Selected checkpoint step: 62000 |
|
|
| ## LIBERO Evaluation |
|
|
| Full-suite LIBERO evaluation used 50 trials per task over 40 tasks: |
|
|
| | Suite | Successes | Success Rate | |
| | --- | ---: | ---: | |
| | LIBERO-Spatial | 493 / 500 | 98.60% | |
| | LIBERO-Object | 496 / 500 | 99.20% | |
| | LIBERO-Goal | 496 / 500 | 99.20% | |
| | LIBERO-10 | 477 / 500 | 95.40% | |
| | Overall | 1962 / 2000 | 98.10% | |
|
|
| The corresponding evaluation records are included under `eval/`. |
|
|
| ## Download |
|
|
| ```bash |
| pip install -U huggingface_hub |
| |
| huggingface-cli download AmberJar/PFD \ |
| libero_pfd_action512_partial_12x12_step62000.pt \ |
| config.yaml \ |
| dataset_stats.json \ |
| eval/summary.json \ |
| eval/task_success_rates.csv \ |
| --local-dir ./checkpoints/pfd_libero_12x12_step62000 |
| ``` |
|
|
| ## Evaluation Command |
|
|
| From the PFD code repository: |
|
|
| ```bash |
| export DIFFSYNTH_MODEL_BASE_PATH="$(pwd)/checkpoints" |
| export DIFFSYNTH_SKIP_DOWNLOAD=true |
| export LIBERO_CONFIG_PATH="$(pwd)/.libero_scratch" |
| |
| python experiments/libero/run_libero_manager.py \ |
| task=libero_uncond_2cam224_1e-4 \ |
| model=fastwam_pfd_action512_partial \ |
| ckpt=./checkpoints/pfd_libero_12x12_step62000/libero_pfd_action512_partial_12x12_step62000.pt \ |
| EVALUATION.dataset_stats_path=./checkpoints/pfd_libero_12x12_step62000/dataset_stats.json \ |
| EVALUATION.num_trials=50 \ |
| MULTIRUN.num_gpus=8 \ |
| model.pfd.partial_unfreeze.action_last_layers=12 \ |
| model.pfd.partial_unfreeze.video_last_layers=12 |
| ``` |
|
|
| ## Integrity |
|
|
| See `SHA256SUMS` and `manifest.json` for file hashes and provenance. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{fang2026pfd, |
| title={Privileged Foresight Distillation: Zero-Cost Future Correction for World Action Models}, |
| author={Fang, Pengcheng and Chen, Hongli and Cai, Xiaohao}, |
| journal={arXiv preprint arXiv:2604.25859}, |
| year={2026} |
| } |
| ``` |
|
|
