CowAndSheep/timerewarder-demos

TimeRewarder — MetaWorld expert demos

Scripted-policy expert videos for 10 MetaWorld tasks, used to train TimeRewarder, from the paper:

TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance Yuyang Liu*, Chuan Wen*, Yihang Hu, Dinesh Jayaraman, Yang Gao†

🌐 Project Page · 📄 Paper · 💻 Code · 🤗 Checkpoints

Layout

One folder per task; 100 train + 100 held-out demos each:

<task-id>/
  videos/*.mp4     # demoN.mp4 (train) + <task>_N.mp4 (all kept)
  label.txt        # train split   (lines: "<video> 0")
  label_val.txt    # held-out split
  text.csv         # task language description

huggingface-cli download CowAndSheep/timerewarder-demos --repo-type dataset --local-dir demos

Point the training pipeline's demo root at the downloaded directory (see the code repository).

Citation

@article{liu2025timerewarder,
  title={TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance},
  author={Liu, Yuyang and Wen, Chuan and Hu, Yihang and Jayaraman, Dinesh and Gao, Yang},
  journal={arXiv preprint arXiv:2509.26627},
  year={2025}
}