| Name | rewind-reward-pytorch JSON |
| Version |
0.0.12
JSON |
| download |
| home_page | None |
| Summary | Rewind Reward |
| upload_time | 2025-08-09 00:40:28 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2025 Phil Wang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
artificial intelligence
deep learning
robotics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<img src="./rewind.png" width="400px"></img>
<img src="./fig9.png" width="400px"></img>
## ReWiND Reward - Pytorch (wip)
Implementation of [ReWiND, "Language-Guided Rewards Teach Robot Policies without New Demonstrations"](https://rewind-reward.github.io/), from USC / Amazon Robotics
## Install
```bash
$ pip install rewind-reward-pytorch
```
## Usage
```python
import torch
from rewind_reward_pytorch import RewardModel
reward_model = RewardModel()
commands = [
'pick up the blue ball and put it in the red tray',
'pick up the red cube and put it in the green bin'
]
videos = torch.rand(2, 3, 16, 224, 224)
loss = reward_model(commands, videos, rewards = torch.randn(2, 16))
loss.backward()
# after much training
pred = reward_model(commands, videos)
assert pred.shape == (2, 16)
```
## Citations
```bibtex
@article{Zhang2025ReWiNDLR,
title = {ReWiND: Language-Guided Rewards Teach Robot Policies without New Demonstrations},
author = {Jiahui Zhang and Yusen Luo and Abrar Anwar and Sumedh Anand Sontakke and Joseph J. Lim and Jesse Thomason and Erdem Biyik and Jesse Zhang},
journal = {ArXiv},
year = {2025},
volume = {abs/2505.10911},
url = {https://api.semanticscholar.org/CorpusID:278714746}
}
```
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"description": "\n<img src=\"./rewind.png\" width=\"400px\"></img>\n\n<img src=\"./fig9.png\" width=\"400px\"></img>\n\n## ReWiND Reward - Pytorch (wip)\n\nImplementation of [ReWiND, \"Language-Guided Rewards Teach Robot Policies without New Demonstrations\"](https://rewind-reward.github.io/), from USC / Amazon Robotics\n\n## Install\n\n```bash\n$ pip install rewind-reward-pytorch\n```\n\n## Usage\n\n```python\nimport torch\nfrom rewind_reward_pytorch import RewardModel\n\nreward_model = RewardModel()\n\ncommands = [\n 'pick up the blue ball and put it in the red tray',\n 'pick up the red cube and put it in the green bin'\n]\n\nvideos = torch.rand(2, 3, 16, 224, 224)\n\nloss = reward_model(commands, videos, rewards = torch.randn(2, 16))\n\nloss.backward()\n\n# after much training\n\npred = reward_model(commands, videos)\n\nassert pred.shape == (2, 16)\n```\n\n## Citations\n\n```bibtex\n@article{Zhang2025ReWiNDLR,\n title = {ReWiND: Language-Guided Rewards Teach Robot Policies without New Demonstrations},\n author = {Jiahui Zhang and Yusen Luo and Abrar Anwar and Sumedh Anand Sontakke and Joseph J. Lim and Jesse Thomason and Erdem Biyik and Jesse Zhang},\n journal = {ArXiv},\n year = {2025},\n volume = {abs/2505.10911},\n url = {https://api.semanticscholar.org/CorpusID:278714746}\n}\n```\n",
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