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@article{qian2026scaling,
  title     = {Scaling Spatial and Temporal Context for Robotic Imitation Learning Policies With Scene Graphs},
  author    = {Qian, Jianing and Peng, Qinhe and Panov, Emmanuel and Fermoselle, Leonor and Jayaraman, Dinesh* and Bucher, Bernadette* and Kelestemur, Tarik*},
  year      = {2026},
  url       = {https://openreview.net/forum?id=WjVTkXDc2T},
  journal   = {CVPR},
  abstract  = {Imitation learning enables robots to learn how to execute tasks via observation. However, real-world environments like homes and offices are often severely partially observed due to their large spatial scales. In addition, many tasks involve executing a series of subtasks requiring autonomous robots to reason over extended time horizons. To address these challenges, we propose using scene graphs as an explicit and structured memory mechanism in imitation learning. By maintaining a dynamic scene graph that captures object-centric relationships and their evolution over time, our method allows the agent

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 = './'

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# 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__)

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%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

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  // 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);