Rewind Reward
Project description
ReWiND Reward - Pytorch (wip)
Implementation of ReWiND, "Language-Guided Rewards Teach Robot Policies without New Demonstrations", from USC / Amazon Robotics
Install
$ pip install rewind-reward-pytorch
Usage
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
@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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