dineshj1

@article{open_x_embodiment_rt_x_2024,
title={Open {X-E}mbodiment: Robotic Learning Datasets and {RT-X} Models},
author = {Open X-Embodiment Collaboration and Abby O'Neill and Abdul Rehman and Abhiram Maddukuri and Abhishek Gupta and Abhishek Padalkar and Abraham Lee and Acorn Pooley and Agrim Gupta and Ajay Mandlekar and Ajinkya Jain and Albert Tung and Alex Bewley and Alex Herzog and Alex Irpan and Alexander Khazatsky and Anant Rai and Anchit Gupta and Andrew Wang and Andrey Kolobov and Anikait Singh and Animesh Garg and Aniruddha Kembhavi and Annie Xie and Anthony Brohan and Antonin Raffin and Archit Sharma and Arefeh Yavary and Arhan Jain and Ashwin Balakrishna and Ayzaan Wahid and Ben Burgess-Limerick and Beomjoon Kim and Bernhard Schölkopf and Blake Wulfe and Brian Ichter and Cewu Lu and Charles Xu and Charlotte Le and Chelsea Finn and Chen Wang and Chenfeng Xu and Cheng Chi and Chenguang Huang and Christine Chan and Christopher Agia and Chuer Pan and Chuyuan Fu and Coline Devin and Danfei Xu and Daniel 

dineshj1

# First make sure you pip install google-api-python-client
from apiclient.discovery import build
from httplib2 import Http
from oauth2client import file, client, tools

def store_opts(opt):  # opt is the object returned by argparse
    params = dict(opt.__dict__)
    
   # here I remove parameters I don't want recorded in google sheets
    params.pop('retain_in_test', None)

dineshj1

# Written with help from http://warmspringwinds.github.io/tensorflow/tf-slim
# /2016/12/21/tfrecords-guide/
import os

import ipdb
import numpy as np
import tensorflow as tf
from tqdm import tqdm

tf_dir = './'

dineshj1

# Dinesh Jayaraman

# Based on code by
# Authors: Fabian Pedregosa <fabian.pedregosa@inria.fr>
#          Olivier Grisel <olivier.grisel@ensta.org>
#          Mathieu Blondel <mathieu@mblondel.org>
#          Gael Varoquaux
# License: BSD 3 clause (C) INRIA 2011

print(__doc__)

dineshj1

%demo_textprogressbar
%This a demo for textprogressbar script
textprogressbar('calculating outputs: ');
for i=1:100,
    textprogressbar(i);
    pause(0.1);
end
textprogressbar('done');

dineshj1

import argparse
import time

start_time=time.time();
################## Argument Parsing #####################################
parser=argparse.ArgumentParser();
parser.add_argument('-s','--solver', default='', type=str); # if empty, solver is created, else read
parser.add_argument('-res', '--resume_from', default='', type=str); #if not empty, resumes training from given file
parser.add_argument('-ft', '--finetune_from', default='', type=str);

dineshj1

function submitjobs(nosubmit, numjobs, main)
  try
    xlwrite_path='../data_utils/xlwrite/';
    addpath(xlwrite_path);
    javaaddpath([xlwrite_path 'poi_library/poi-3.8-20120326.jar']);
    javaaddpath([xlwrite_path 'poi_library/poi-ooxml-3.8-20120326.jar']);
    javaaddpath([xlwrite_path 'poi_library/poi-ooxml-schemas-3.8-20120326.jar']);
    javaaddpath([xlwrite_path 'poi_library/xmlbeans-2.3.0.jar']);
    javaaddpath([xlwrite_path 'poi_library/dom4j-1.6.1.jar']);
    javaaddpath([xlwrite_path 'poi_library/stax-api-1.0.1.jar']);

dineshj1

from tsne import bh_sne
import numpy as np
from skimage.transform import resize

from matplotlib import pyplot as plt

def gray_to_color(img):
    if len(img.shape) == 2:
        img = np.dstack((img, img, img))
    return img

dineshj1

  // generate [0..n-1]
  auto seq = [](size_t n) -> std::vector<size_t> {
      std::vector<size_t> v(n);
      for (size_t i=0; i<n; ++i) v[i] = i;
      return v;
  };
  auto index = seq(n);

  // n * n distance matrix
  std::vector<D> dists(n * n);