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@article{gao2025vlmgineer, | |
title={VLMgineer: Vision-Language Models as Robotic Toolsmiths}, | |
author={Gao, George Jiayuan and Li, Tianyu and Shi, Junyao and Li, Yihan and Zhang, Zizhe and Figueroa, Nadia and Jayaraman, Dinesh}, | |
journal={(Under review at NeurIPS)}, | |
year={2025}, | |
url={https://openreview.net/forum?id=bBed9j01Dk}, | |
abstract={Tool design and use reflect the ability to understand and manipulate the physical world through creativity, planning, and foresight. As such, it is often regarded as a measurable indicator of cognitive intelligence across biological species. While much of today's research on robotics intelligence focuses on generating better control strategies, inventing smarter tools offers a complementary form of physical intelligence: moving the problem-solving onus into the tool's geometry so that control becomes simpler. This motivates us to ask: can today's foundation models offer useful priors to automatically invent—and effectively wield—such tools? We present VLMgineer, a fra |
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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); |