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,
RewindTrainWrapper
)
reward_model = RewardModel(
reward_bins = 10,
categorical_rewards = False,
)
wrapper = RewindTrainWrapper(
reward_model,
rewind_augmentation_prob = 0.25
)
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',
]
video = torch.rand(2, 3, 16, 224, 224)
loss = wrapper(
commands,
video
)
loss.backward()
# after much training
pred_rewards = reward_model(
commands,
video
) # (2, 16) - per frame progress prediction
assert pred_rewards.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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