| --- |
| license: mit |
| tags: |
| - reinforcement-learning |
| - reward-model |
| - robotics |
| - metaworld |
| - learning-from-videos |
| --- |
| |
| # TimeRewarder — MetaWorld checkpoints |
|
|
| Pretrained **TimeRewarder** checkpoints for 10 MetaWorld tasks, from the paper: |
|
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| **[TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance](https://arxiv.org/abs/2509.26627)** |
| Yuyang Liu\*, Chuan Wen\*, Yihang Hu, Dinesh Jayaraman, Yang Gao† |
|
|
| [🌐 Project Page](https://timerewarder.github.io/) · [📄 Paper](https://arxiv.org/abs/2509.26627) · [💻 Code](https://github.com/CowAndSheep/TimeRewader) · [🤗 Demos](https://huggingface.co/datasets/CowAndSheep/timerewarder-demos) |
|
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| TimeRewarder learns a dense reward from passive (action-free) videos by predicting the frame-wise temporal distance between frames; the per-frame progress prediction is used directly as the reward for downstream RL. |
|
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| ## Contents |
|
|
| One vision-only checkpoint per task (`<task>_20bins.pth`, 20 discrete progress bins): |
| `basketball`, `button-press-topdown`, `disassemble`, `door-open`, `drawer-open`, `lever-pull`, `plate-slide`, `stick-push`, `window-close`, `window-open`. |
|
|
| ```bash |
| huggingface-cli download CowAndSheep/timerewarder --local-dir models/ckpt |
| ``` |
|
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| Drop the `.pth` files into `models/ckpt/` and run the downstream RL as described in the [code repository](https://github.com/CowAndSheep/TimeRewader). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
