Pi0-fast delta action chunk trained on VLABench datasets.

This repository provides the Pi0-fast model trained with the whole VLABench's official primitive tasks dataset. To be noticed, this config corresponds to the delta chunk, which is state[1:] - state[:-1] instead of state - state[0].

Evaluation

To run this checkpoint, please clone this repo: https://github.com/Shiduo-zh/openpi, and checkout to the branch main. Assume that you download this checkpoints and put it in the directory checkpoints, to run the policy as server, please run:

bash vla_bench_scipts/serve_policy.sh pi0_ft_vlabench_primitive_aligned checkpoints/VLABench/pi0-fast-ft-primitive-10task-deltachunk/29999/

After serving the policy, open another terminal and run:

bash vla_bench_scipts/multi_run_vlabench.sh <Your path to store the evaluate results>

Train

To reproduce the training result, please run the training script with the config pi0_ft_vlabench_primitive_aligned.

XLA_PYTHON_CLIENT_MEM_FRACTION=0.95 uv run scripts/train.py pi0_ft_vlabench_primitive_aligned --exp-name=pi0_ft_vlabench_primitive_aligned --overwrite

Our checkpoint is trained on 8 H100 for 30k iterations, with 5000 episodes data acrossing 10 tasks.

Reference Results

The reference success rate of this model is:

Track add_condiment insert_flower select_book select_chemistry_tube select_drink select_fruit select_mahjong select_painting select_poker select_toy Avg_SR
track_1_in_distribution 0.48 0.38 0.24 0.62 0.18 0.76 0.52 0.46 0.78 0.7 0.512
track_2_cross_category 0.02 ? 0.04 0.14 0.06 0.74 0.25 0.46 0.72 0.44 0.342
track_3_common_sense 0.44 0.36 0.16 0.88 0.18 0.56 0.02 0.48 0.34 0.52 0.417
track_4_semantic_instruction 0.42 0.34 0.18 0.6 0.08 0.62 0.5 0.5 0.7 0.48 0.442
track_6_unseen_texture 0.56 0.32 0.24 0.52 0.24 0.6 0.36 0.38 0.54 0.58 0.444
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