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@article{shipoints2reward, | |
title={Points2Reward: Robotic Manipulation Rewards from Just One Video}, | |
author={Shi, Junyao and Smith, Joshua and Qian, Jianing and Jayaraman, Dinesh}, | |
journal={(Under review)}, | |
year={2025}, | |
abstract={Detailed and dense reward functions provided clearly graded signals that enable a robot to evaluate and improve its policy, but generating such functions for new tasks is often cumbersome and expertise-intensive. On the other hand, end users can often easily record a video demonstrating the desired object trajectories and goal configurations involved in a new skill, but we lack methods to reliably learn from such a simple specification. We propose “Points2Reward” (P2R), which effectively computes dense rewards from a single video. To do this, P2R tracks task-relevant object points in task demonstrations and policy rollouts, and matches them to compare the desired and achieved object trajectories to generate reward scores. Exploiting recent advances in point tracking and sema |
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# 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) |
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# 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 <[email protected]> | |
# Olivier Grisel <[email protected]> | |
# Mathieu Blondel <[email protected]> | |
# 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'); | |
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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); |
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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']); |
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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); |