Skip to main content

rtx - Pytorch

Project description

Multi-Modality

RT-X

Pytorch implementation of the models RT-1-X and RT-2-X from the paper: "Open X-Embodiment: Robotic Learning Datasets and RT-X Models"

Here we implement both model architectures, RTX-1 and RTX-2

Paper Link

  • The RTX-2 Implementation does not natively output for simplicity a 7 dimensional vector but rather text tokens, if you wanted to output 7 dimensional vector you could implement the same token learner as in RTX1

Appreciation

  • Lucidrains
  • Agorians

Install

pip install rtx-torch

Usage

  • RTX1 Usage takes in text and videos
import torch
from rtx.rtx1 import RTX1

model = RTX1()

video = torch.randn(2, 3, 6, 224, 224)

instructions = ["bring me that apple sitting on the table", "please pass the butter"]

# compute the train logits
train_logits = model.train(video, instructions)

# set the model to evaluation mode
model.model.eval()

# compute the eval logits with a conditional scale of 3
eval_logits = model.run(video, instructions, cond_scale=3.0)
print(eval_logits.shape)
  • RTX-2 takes in images and text and interleaves them to form multi-modal sentences and outputs text tokens not a 7 dimensional vector of x,y,z,roll,pitch,yaw,and gripper
import torch
from rtx import RTX2

# usage
img = torch.randn(1, 3, 256, 256)
text = torch.randint(0, 20000, (1, 1024))

model = RTX2()
output = model(img, text)
print(output)

License

MIT

Citations

@misc{open_x_embodiment_rt_x_2023,
title={Open {X-E}mbodiment: Robotic Learning Datasets and {RT-X} Models},
author = {Open X-Embodiment Collaboration and Abhishek Padalkar and Acorn Pooley and Ajinkya Jain and Alex Bewley and Alex Herzog and Alex Irpan and Alexander Khazatsky and Anant Rai and Anikait Singh and Anthony Brohan and Antonin Raffin and Ayzaan Wahid and Ben Burgess-Limerick and Beomjoon Kim and Bernhard Schölkopf and Brian Ichter and Cewu Lu and Charles Xu and Chelsea Finn and Chenfeng Xu and Cheng Chi and Chenguang Huang and Christine Chan and Chuer Pan and Chuyuan Fu and Coline Devin and Danny Driess and Deepak Pathak and Dhruv Shah and Dieter Büchler and Dmitry Kalashnikov and Dorsa Sadigh and Edward Johns and Federico Ceola and Fei Xia and Freek Stulp and Gaoyue Zhou and Gaurav S. Sukhatme and Gautam Salhotra and Ge Yan and Giulio Schiavi and Hao Su and Hao-Shu Fang and Haochen Shi and Heni Ben Amor and Henrik I Christensen and Hiroki Furuta and Homer Walke and Hongjie Fang and Igor Mordatch and Ilija Radosavovic and Isabel Leal and Jacky Liang and Jaehyung Kim and Jan Schneider and Jasmine Hsu and Jeannette Bohg and Jeffrey Bingham and Jiajun Wu and Jialin Wu and Jianlan Luo and Jiayuan Gu and Jie Tan and Jihoon Oh and Jitendra Malik and Jonathan Tompson and Jonathan Yang and Joseph J. Lim and João Silvério and Junhyek Han and Kanishka Rao and Karl Pertsch and Karol Hausman and Keegan Go and Keerthana Gopalakrishnan and Ken Goldberg and Kendra Byrne and Kenneth Oslund and Kento Kawaharazuka and Kevin Zhang and Keyvan Majd and Krishan Rana and Krishnan Srinivasan and Lawrence Yunliang Chen and Lerrel Pinto and Liam Tan and Lionel Ott and Lisa Lee and Masayoshi Tomizuka and Maximilian Du and Michael Ahn and Mingtong Zhang and Mingyu Ding and Mohan Kumar Srirama and Mohit Sharma and Moo Jin Kim and Naoaki Kanazawa and Nicklas Hansen and Nicolas Heess and Nikhil J Joshi and Niko Suenderhauf and Norman Di Palo and Nur Muhammad Mahi Shafiullah and Oier Mees and Oliver Kroemer and Pannag R Sanketi and Paul Wohlhart and Peng Xu and Pierre Sermanet and Priya Sundaresan and Quan Vuong and Rafael Rafailov and Ran Tian and Ria Doshi and Roberto Martín-Martín and Russell Mendonca and Rutav Shah and Ryan Hoque and Ryan Julian and Samuel Bustamante and Sean Kirmani and Sergey Levine and Sherry Moore and Shikhar Bahl and Shivin Dass and Shuran Song and Sichun Xu and Siddhant Haldar and Simeon Adebola and Simon Guist and Soroush Nasiriany and Stefan Schaal and Stefan Welker and Stephen Tian and Sudeep Dasari and Suneel Belkhale and Takayuki Osa and Tatsuya Harada and Tatsuya Matsushima and Ted Xiao and Tianhe Yu and Tianli Ding and Todor Davchev and Tony Z. Zhao and Travis Armstrong and Trevor Darrell and Vidhi Jain and Vincent Vanhoucke and Wei Zhan and Wenxuan Zhou and Wolfram Burgard and Xi Chen and Xiaolong Wang and Xinghao Zhu and Xuanlin Li and Yao Lu and Yevgen Chebotar and Yifan Zhou and Yifeng Zhu and Ying Xu and Yixuan Wang and Yonatan Bisk and Yoonyoung Cho and Youngwoon Lee and Yuchen Cui and Yueh-hua Wu and Yujin Tang and Yuke Zhu and Yunzhu Li and Yusuke Iwasawa and Yutaka Matsuo and Zhuo Xu and Zichen Jeff Cui},
howpublished  = {\url{https://arxiv.org/abs/2310.08864}},
year = {2023},
}

Todo

  • Integrate Efficient net with RT-1 and RT-2
  • create training script for both models
  • Provide a table of all the datasets

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

rtx_torch-0.0.6.tar.gz (15.2 kB view details)

Uploaded Source

Built Distribution

rtx_torch-0.0.6-py3-none-any.whl (13.5 kB view details)

Uploaded Python 3

File details

Details for the file rtx_torch-0.0.6.tar.gz.

File metadata

  • Download URL: rtx_torch-0.0.6.tar.gz
  • Upload date:
  • Size: 15.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.3.2 CPython/3.11.0 Darwin/22.4.0

File hashes

Hashes for rtx_torch-0.0.6.tar.gz
Algorithm Hash digest
SHA256 25f07141b0df7c61b3f7060bd1f85088a4e199fca09840e9ca19977c2955550d
MD5 feb5c7d9dcfb0dd734b30d5580ccc919
BLAKE2b-256 bd43231c557811b0cee167c3f4b1457b2d7bf4b67ece901623636fc5a9330e0d

See more details on using hashes here.

File details

Details for the file rtx_torch-0.0.6-py3-none-any.whl.

File metadata

  • Download URL: rtx_torch-0.0.6-py3-none-any.whl
  • Upload date:
  • Size: 13.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.3.2 CPython/3.11.0 Darwin/22.4.0

File hashes

Hashes for rtx_torch-0.0.6-py3-none-any.whl
Algorithm Hash digest
SHA256 0c8c758e8a11d2188e8cd4b3a7dfa447c43cb027e59738df4fec66e09ac5088a
MD5 4a72841250d11ee3313580d2ecec9218
BLAKE2b-256 19e4a46f56a2bc54bb81eeab6c548cadb8c9a7f0f27472e036a409dc1c8d7a29

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page