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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.06613">arXiv:2409.06613</a> <span> [<a href="https://arxiv.org/pdf/2409.06613">pdf</a>, <a href="https://arxiv.org/format/2409.06613">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> DemoStart: Demonstration-led auto-curriculum applied to sim-to-real with multi-fingered robots </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bauza%2C+M">Maria Bauza</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+J+E">Jose Enrique Chen</a>, <a href="/search/cs?searchtype=author&query=Dalibard%2C+V">Valentin Dalibard</a>, <a href="/search/cs?searchtype=author&query=Gileadi%2C+N">Nimrod Gileadi</a>, <a href="/search/cs?searchtype=author&query=Hafner%2C+R">Roland Hafner</a>, <a href="/search/cs?searchtype=author&query=Martins%2C+M+F">Murilo F. Martins</a>, <a href="/search/cs?searchtype=author&query=Moore%2C+J">Joss Moore</a>, <a href="/search/cs?searchtype=author&query=Pevceviciute%2C+R">Rugile Pevceviciute</a>, <a href="/search/cs?searchtype=author&query=Laurens%2C+A">Antoine Laurens</a>, <a href="/search/cs?searchtype=author&query=Rao%2C+D">Dushyant Rao</a>, <a href="/search/cs?searchtype=author&query=Zambelli%2C+M">Martina Zambelli</a>, <a href="/search/cs?searchtype=author&query=Riedmiller%2C+M">Martin Riedmiller</a>, <a href="/search/cs?searchtype=author&query=Scholz%2C+J">Jon Scholz</a>, <a href="/search/cs?searchtype=author&query=Bousmalis%2C+K">Konstantinos Bousmalis</a>, <a href="/search/cs?searchtype=author&query=Nori%2C+F">Francesco Nori</a>, <a href="/search/cs?searchtype=author&query=Heess%2C+N">Nicolas Heess</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.06613v2-abstract-short" style="display: inline;"> We present DemoStart, a novel auto-curriculum reinforcement learning method capable of learning complex manipulation behaviors on an arm equipped with a three-fingered robotic hand, from only a sparse reward and a handful of demonstrations in simulation. Learning from simulation drastically reduces the development cycle of behavior generation, and domain randomization techniques are leveraged to a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06613v2-abstract-full').style.display = 'inline'; document.getElementById('2409.06613v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.06613v2-abstract-full" style="display: none;"> We present DemoStart, a novel auto-curriculum reinforcement learning method capable of learning complex manipulation behaviors on an arm equipped with a three-fingered robotic hand, from only a sparse reward and a handful of demonstrations in simulation. Learning from simulation drastically reduces the development cycle of behavior generation, and domain randomization techniques are leveraged to achieve successful zero-shot sim-to-real transfer. Transferred policies are learned directly from raw pixels from multiple cameras and robot proprioception. Our approach outperforms policies learned from demonstrations on the real robot and requires 100 times fewer demonstrations, collected in simulation. More details and videos in https://sites.google.com/view/demostart. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06613v2-abstract-full').style.display = 'none'; document.getElementById('2409.06613v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages total with 7 pages of appendix. 9 Figures, 4 in the main text and 5 in the appendix</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.11706">arXiv:2306.11706</a> <span> [<a href="https://arxiv.org/pdf/2306.11706">pdf</a>, <a href="https://arxiv.org/format/2306.11706">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bousmalis%2C+K">Konstantinos Bousmalis</a>, <a href="/search/cs?searchtype=author&query=Vezzani%2C+G">Giulia Vezzani</a>, <a href="/search/cs?searchtype=author&query=Rao%2C+D">Dushyant Rao</a>, <a href="/search/cs?searchtype=author&query=Devin%2C+C">Coline Devin</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+A+X">Alex X. Lee</a>, <a href="/search/cs?searchtype=author&query=Bauza%2C+M">Maria Bauza</a>, <a href="/search/cs?searchtype=author&query=Davchev%2C+T">Todor Davchev</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+Y">Yuxiang Zhou</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+A">Agrim Gupta</a>, <a href="/search/cs?searchtype=author&query=Raju%2C+A">Akhil Raju</a>, <a href="/search/cs?searchtype=author&query=Laurens%2C+A">Antoine Laurens</a>, <a href="/search/cs?searchtype=author&query=Fantacci%2C+C">Claudio Fantacci</a>, <a href="/search/cs?searchtype=author&query=Dalibard%2C+V">Valentin Dalibard</a>, <a href="/search/cs?searchtype=author&query=Zambelli%2C+M">Martina Zambelli</a>, <a href="/search/cs?searchtype=author&query=Martins%2C+M">Murilo Martins</a>, <a href="/search/cs?searchtype=author&query=Pevceviciute%2C+R">Rugile Pevceviciute</a>, <a href="/search/cs?searchtype=author&query=Blokzijl%2C+M">Michiel Blokzijl</a>, <a href="/search/cs?searchtype=author&query=Denil%2C+M">Misha Denil</a>, <a href="/search/cs?searchtype=author&query=Batchelor%2C+N">Nathan Batchelor</a>, <a href="/search/cs?searchtype=author&query=Lampe%2C+T">Thomas Lampe</a>, <a href="/search/cs?searchtype=author&query=Parisotto%2C+E">Emilio Parisotto</a>, <a href="/search/cs?searchtype=author&query=%C5%BBo%C5%82na%2C+K">Konrad 呕o艂na</a>, <a href="/search/cs?searchtype=author&query=Reed%2C+S">Scott Reed</a>, <a href="/search/cs?searchtype=author&query=Colmenarejo%2C+S+G">Sergio G贸mez Colmenarejo</a>, <a href="/search/cs?searchtype=author&query=Scholz%2C+J">Jon Scholz</a> , et al. (14 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.11706v2-abstract-short" style="display: inline;"> The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to transform robot learning. Inspired by recent advances in foundation models for vision and language, we propose a multi-embodiment, multi-task generalist agent for robotic manipulation. This agent, named RoboCat, is a visual goal-conditioned de… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.11706v2-abstract-full').style.display = 'inline'; document.getElementById('2306.11706v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.11706v2-abstract-full" style="display: none;"> The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to transform robot learning. Inspired by recent advances in foundation models for vision and language, we propose a multi-embodiment, multi-task generalist agent for robotic manipulation. This agent, named RoboCat, is a visual goal-conditioned decision transformer capable of consuming action-labelled visual experience. This data spans a large repertoire of motor control skills from simulated and real robotic arms with varying sets of observations and actions. With RoboCat, we demonstrate the ability to generalise to new tasks and robots, both zero-shot as well as through adaptation using only 100-1000 examples for the target task. We also show how a trained model itself can be used to generate data for subsequent training iterations, thus providing a basic building block for an autonomous improvement loop. We investigate the agent's capabilities, with large-scale evaluations both in simulation and on three different real robot embodiments. We find that as we grow and diversify its training data, RoboCat not only shows signs of cross-task transfer, but also becomes more efficient at adapting to new tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.11706v2-abstract-full').style.display = 'none'; document.getElementById('2306.11706v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Transactions on Machine Learning Research (12/2023)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.06192">arXiv:2110.06192</a> <span> [<a href="https://arxiv.org/pdf/2110.06192">pdf</a>, <a href="https://arxiv.org/format/2110.06192">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lee%2C+A+X">Alex X. Lee</a>, <a href="/search/cs?searchtype=author&query=Devin%2C+C">Coline Devin</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+Y">Yuxiang Zhou</a>, <a href="/search/cs?searchtype=author&query=Lampe%2C+T">Thomas Lampe</a>, <a href="/search/cs?searchtype=author&query=Bousmalis%2C+K">Konstantinos Bousmalis</a>, <a href="/search/cs?searchtype=author&query=Springenberg%2C+J+T">Jost Tobias Springenberg</a>, <a href="/search/cs?searchtype=author&query=Byravan%2C+A">Arunkumar Byravan</a>, <a href="/search/cs?searchtype=author&query=Abdolmaleki%2C+A">Abbas Abdolmaleki</a>, <a href="/search/cs?searchtype=author&query=Gileadi%2C+N">Nimrod Gileadi</a>, <a href="/search/cs?searchtype=author&query=Khosid%2C+D">David Khosid</a>, <a href="/search/cs?searchtype=author&query=Fantacci%2C+C">Claudio Fantacci</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+J+E">Jose Enrique Chen</a>, <a href="/search/cs?searchtype=author&query=Raju%2C+A">Akhil Raju</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+R">Rae Jeong</a>, <a href="/search/cs?searchtype=author&query=Neunert%2C+M">Michael Neunert</a>, <a href="/search/cs?searchtype=author&query=Laurens%2C+A">Antoine Laurens</a>, <a href="/search/cs?searchtype=author&query=Saliceti%2C+S">Stefano Saliceti</a>, <a href="/search/cs?searchtype=author&query=Casarini%2C+F">Federico Casarini</a>, <a href="/search/cs?searchtype=author&query=Riedmiller%2C+M">Martin Riedmiller</a>, <a href="/search/cs?searchtype=author&query=Hadsell%2C+R">Raia Hadsell</a>, <a href="/search/cs?searchtype=author&query=Nori%2C+F">Francesco Nori</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2110.06192v2-abstract-short" style="display: inline;"> We study the problem of robotic stacking with objects of complex geometry. We propose a challenging and diverse set of such objects that was carefully designed to require strategies beyond a simple "pick-and-place" solution. Our method is a reinforcement learning (RL) approach combined with vision-based interactive policy distillation and simulation-to-reality transfer. Our learned policies can ef… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.06192v2-abstract-full').style.display = 'inline'; document.getElementById('2110.06192v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.06192v2-abstract-full" style="display: none;"> We study the problem of robotic stacking with objects of complex geometry. We propose a challenging and diverse set of such objects that was carefully designed to require strategies beyond a simple "pick-and-place" solution. Our method is a reinforcement learning (RL) approach combined with vision-based interactive policy distillation and simulation-to-reality transfer. Our learned policies can efficiently handle multiple object combinations in the real world and exhibit a large variety of stacking skills. In a large experimental study, we investigate what choices matter for learning such general vision-based agents in simulation, and what affects optimal transfer to the real robot. We then leverage data collected by such policies and improve upon them with offline RL. A video and a blog post of our work are provided as supplementary material. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.06192v2-abstract-full').style.display = 'none'; document.getElementById('2110.06192v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">CoRL 2021. Video: https://dpmd.ai/robotics-stacking-YT . Blog: https://dpmd.ai/robotics-stacking . Code: https://github.com/deepmind/rgb_stacking</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/license/index.html">Copyright</a></li> <li><a href="https://info.arxiv.org/help/policies/privacy_policy.html">Privacy Policy</a></li> </ul> </div> <div class="column sorry-app-links"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/web_accessibility.html">Web Accessibility Assistance</a></li> <li> <p class="help"> <a class="a11y-main-link" href="https://status.arxiv.org" target="_blank">arXiv Operational Status <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 512" class="icon filter-dark_grey" role="presentation"><path d="M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z"/></svg></a><br> Get status notifications via <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/email/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>email</a> or <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/slack/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" class="icon filter-black" role="presentation"><path d="M94.12 315.1c0 25.9-21.16 47.06-47.06 47.06S0 341 0 315.1c0-25.9 21.16-47.06 47.06-47.06h47.06v47.06zm23.72 0c0-25.9 21.16-47.06 47.06-47.06s47.06 21.16 47.06 47.06v117.84c0 25.9-21.16 47.06-47.06 47.06s-47.06-21.16-47.06-47.06V315.1zm47.06-188.98c-25.9 0-47.06-21.16-47.06-47.06S139 32 164.9 32s47.06 21.16 47.06 47.06v47.06H164.9zm0 23.72c25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06H47.06C21.16 243.96 0 222.8 0 196.9s21.16-47.06 47.06-47.06H164.9zm188.98 47.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06h-47.06V196.9zm-23.72 0c0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06V79.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06V196.9zM283.1 385.88c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06v-47.06h47.06zm0-23.72c-25.9 0-47.06-21.16-47.06-47.06 0-25.9 21.16-47.06 47.06-47.06h117.84c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06H283.1z"/></svg>slack</a> </p> </li> </ul> </div> </div> </div>  </div> </footer> <script src="https://static.arxiv.org/static/base/1.0.0a5/js/member_acknowledgement.js"></script> </body> </html>

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