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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" xmlns:fb="http://ogp.me/ns/fb#">
<head>
<title>Sarah Bechtle</title>
<meta charset="ISO-8859-1" />
<meta name="author" content="Sarah Bechtle">
<meta http-equiv="content-type" content="text/html; charset=ISO-8859-1"/>
<meta property="og:image" content="titelbild.jpg" />
<link rel="stylesheet" href="main.css" type="text/css">
<script src="main.js" type = "text/javascript"></script>
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<body>
<div id='profile_picture'><img src="cropped_img/img.png" alt="profile_picture"> </div>
<div id='name_title'> Sarah Bechtle</div>
<div id='about'>About me:</div>
<div id='info_text'> I am a last year PhD candidate at the <a href='https://is.mpg.de'>Max Planck Institute for intelligent Systems in Tuebingen</a>. I'm affiliated with the <a href="http://www-clmc.usc.edu">Computational Learning and Motor Control Lab</a>, USC and the <a href="https://wp.nyu.edu/machinesinmotion/">Machines in Motion Lab</a>, NYU. <br><br>My research interests are at the intersection between machine learning and robotics - developing learning algorithms that can be deployed on robots. Specifically I'm interested in model based learning within the action-perception-learning loop of artificial agents with special interest in meta and lifelong learning. <br> <br> I'm currently seeking research opportunities for projects associated with robotics, machine learning and computational neuroscience.
<p id='cv'>
<a href="cv_sarah_bechtle.pdf">cv</a><a href="https://scholar.google.com/citations?user=Hxic_iwAAAAJ&hl=de">google scholar</a><a href="mailto:[email protected]">email</a>
</p>
</div>
<div id='recent_news'>Recent news:</div>
<div id='recent_news_text'>
[March 2021] Our paper Leveraging Forward Model Prediction Error for Learning Control got accepted to ICRA<br><br>
[January 2021] Our paper Meta-learning via learned loss won the overall best student paper award at ICPR<br><br>
[January 2021] We are organizing a workshop on Learning to Learn for Robots at ICRA 2021<br><br>
[December 2020] We open sourced our learning to learn <a href='https://github.com/facebookresearch/LearningToLearn/'>code</a><br><br>
[December 2020] We are organizing a workshop on Learning to Learn at ICLR 2021
</div>
<div id='pub_title'>Publications:</div>
<ol id='pub_text'>
<li>
<strong>Leveraging Forward Model Prediction Error for Learning Control</strong><br>
Bechtle, S., Hammoud, B., Rai, A., Meier, F. and Righetti, L.<br>
<em>IEEE International Conference on Robotics and Automation (ICRA), 2021</em>
<p><a href="https://arxiv.org/pdf/2011.03859.pdf">[Paper]</a><a href="https://www.youtube.com/watch?v=P3WAdhZv-Y4">[Video]</a></p>
</li>
<li>
<strong>Meta-learning via learned loss</strong><br>
Bechtle, S., Molchanov, A., Chebotar, Y., Grefenstette, E., Righetti, L., Sukhatme, G. and Meier, F.<br>
<em>IEEE International Conference on Pattern Recognition.</em> <br>
<p style='color: #6495ED;font-weight:bold;'>Overall best student paper award</p>
<p><a href="https://arxiv.org/pdf/1906.05374.pdf">[Paper]</a><a href="https://www.youtube.com/watch?v=QmePrknJh1g">[Video]</a> <a href="https://github.com/facebookresearch/LearningToLearn/tree/main/ml3">[Code]</a><a href="https://sites.google.com/view/mlthree/">[Website]</a> </p>
</li>
<li>
<strong>Model-Based Inverse Reinforcement Learning from Visual Demonstrations</strong><br>
Das, N., Bechtle, S., Davchev, T., Jayaraman, D., Rai, A. and Meier, F.<br>
<em>In Conference on Robot Learning, 2020</em>
<p><a href="https://drive.google.com/file/d/1_uu4RJbAdYU_4Is_gSVTCO8KA77Mwii1/view">[Paper]</a><a href="https://www.youtube.com/watch?v=sRrNhtLk12M">[Video]</a> <a href="https://github.com/facebookresearch/LearningToLearn/tree/main/mbirl">[Code]</a> </p>
</li>
<li>
<strong>Learning Extended Body Schemas from Visual Keypoints for Object Manipulation</strong><br>
Bechtle, S., Das, N. and Meier, F.<br>
<em>arXiv preprint, 2020</em>
<p><a href="https://arxiv.org/pdf/2011.03882.pdf">[Paper]</a><a href="https://www.youtube.com/watch?v=mqjUvmxd5Vo">[Video]</a> </p>
</li>
<li>
<strong>Curious ilqr: Resolving uncertainty in model-based rl</strong><br>
Bechtle, S., Lin, Y., Rai, A., Righetti, L. and Meier, F.<br>
<em>In Conference on Robot Learning, 2019</em>
<p><a href="http://proceedings.mlr.press/v100/bechtle20a/bechtle20a.pdf">[Paper]</a><a href="http://proceedings.mlr.press/v100/bechtle20a.html">[Code and Video]</a> </p>
</li>
</ol>
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