Root Cause: Training data collected from fixed initial state → model learned specific scenario perfectly but cannot generalize to randomized evaluation states.

Solution: Requires diverse demonstration data with varied initial states.

Model Comparison

Model	Val Loss	Improvement
Standard ACT	0.1289	baseline
Modified ACT	0.0931	27.8% better

Usage

python

import torch
from huggingface_hub import hf_hub_download

# Download checkpoint
checkpoint_path = hf_hub_download(
    repo_id="aryannzzz/act-metaworld-shelf-standard",
    filename="best_model.pth"
)

# Load model
checkpoint = torch.load(checkpoint_path, weights_only=False)
# model.load_state_dict(checkpoint['model_state_dict'])

Files

best_model.pth: Model checkpoint (contains model_state_dict, optimizer_state_dict, and training stats)
norm_stats.npz: Normalization statistics (state_mean, state_std, action_mean, action_std)
config.json: Model configuration

Citation

Based on "Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware" (Zhao et al., RSS 2023)

bibtex

@article{zhao2023learning,
  title={Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware},
  author={Zhao, Tony Z and others},
  journal={RSS},
  year={2023}
}

License

MIT

Contact

For questions or issues, please open an issue in the repository.