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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/2410.21276">arXiv:2410.21276</a> <span> [<a href="https://arxiv.org/pdf/2410.21276">pdf</a>, <a href="https://arxiv.org/format/2410.21276">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> GPT-4o System Card </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=OpenAI"> OpenAI</a>, <a href="/search/cs?searchtype=author&query=%3A"> :</a>, <a href="/search/cs?searchtype=author&query=Hurst%2C+A">Aaron Hurst</a>, <a href="/search/cs?searchtype=author&query=Lerer%2C+A">Adam Lerer</a>, <a href="/search/cs?searchtype=author&query=Goucher%2C+A+P">Adam P. Goucher</a>, <a href="/search/cs?searchtype=author&query=Perelman%2C+A">Adam Perelman</a>, <a href="/search/cs?searchtype=author&query=Ramesh%2C+A">Aditya Ramesh</a>, <a href="/search/cs?searchtype=author&query=Clark%2C+A">Aidan Clark</a>, <a href="/search/cs?searchtype=author&query=Ostrow%2C+A">AJ Ostrow</a>, <a href="/search/cs?searchtype=author&query=Welihinda%2C+A">Akila Welihinda</a>, <a href="/search/cs?searchtype=author&query=Hayes%2C+A">Alan Hayes</a>, <a href="/search/cs?searchtype=author&query=Radford%2C+A">Alec Radford</a>, <a href="/search/cs?searchtype=author&query=M%C4%85dry%2C+A">Aleksander M膮dry</a>, <a href="/search/cs?searchtype=author&query=Baker-Whitcomb%2C+A">Alex Baker-Whitcomb</a>, <a href="/search/cs?searchtype=author&query=Beutel%2C+A">Alex Beutel</a>, <a href="/search/cs?searchtype=author&query=Borzunov%2C+A">Alex Borzunov</a>, <a href="/search/cs?searchtype=author&query=Carney%2C+A">Alex Carney</a>, <a href="/search/cs?searchtype=author&query=Chow%2C+A">Alex Chow</a>, <a href="/search/cs?searchtype=author&query=Kirillov%2C+A">Alex Kirillov</a>, <a href="/search/cs?searchtype=author&query=Nichol%2C+A">Alex Nichol</a>, <a href="/search/cs?searchtype=author&query=Paino%2C+A">Alex Paino</a>, <a href="/search/cs?searchtype=author&query=Renzin%2C+A">Alex Renzin</a>, <a href="/search/cs?searchtype=author&query=Passos%2C+A+T">Alex Tachard Passos</a>, <a href="/search/cs?searchtype=author&query=Kirillov%2C+A">Alexander Kirillov</a>, <a href="/search/cs?searchtype=author&query=Christakis%2C+A">Alexi Christakis</a> , et al. (395 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="2410.21276v1-abstract-short" style="display: inline;"> GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 mil… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21276v1-abstract-full').style.display = 'inline'; document.getElementById('2410.21276v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.21276v1-abstract-full" style="display: none;"> GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21276v1-abstract-full').style.display = 'none'; document.getElementById('2410.21276v1-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> 25 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.08774">arXiv:2303.08774</a> <span> [<a href="https://arxiv.org/pdf/2303.08774">pdf</a>, <a href="https://arxiv.org/format/2303.08774">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> GPT-4 Technical Report </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=OpenAI"> OpenAI</a>, <a href="/search/cs?searchtype=author&query=Achiam%2C+J">Josh Achiam</a>, <a href="/search/cs?searchtype=author&query=Adler%2C+S">Steven Adler</a>, <a href="/search/cs?searchtype=author&query=Agarwal%2C+S">Sandhini Agarwal</a>, <a href="/search/cs?searchtype=author&query=Ahmad%2C+L">Lama Ahmad</a>, <a href="/search/cs?searchtype=author&query=Akkaya%2C+I">Ilge Akkaya</a>, <a href="/search/cs?searchtype=author&query=Aleman%2C+F+L">Florencia Leoni Aleman</a>, <a href="/search/cs?searchtype=author&query=Almeida%2C+D">Diogo Almeida</a>, <a href="/search/cs?searchtype=author&query=Altenschmidt%2C+J">Janko Altenschmidt</a>, <a href="/search/cs?searchtype=author&query=Altman%2C+S">Sam Altman</a>, <a href="/search/cs?searchtype=author&query=Anadkat%2C+S">Shyamal Anadkat</a>, <a href="/search/cs?searchtype=author&query=Avila%2C+R">Red Avila</a>, <a href="/search/cs?searchtype=author&query=Babuschkin%2C+I">Igor Babuschkin</a>, <a href="/search/cs?searchtype=author&query=Balaji%2C+S">Suchir Balaji</a>, <a href="/search/cs?searchtype=author&query=Balcom%2C+V">Valerie Balcom</a>, <a href="/search/cs?searchtype=author&query=Baltescu%2C+P">Paul Baltescu</a>, <a href="/search/cs?searchtype=author&query=Bao%2C+H">Haiming Bao</a>, <a href="/search/cs?searchtype=author&query=Bavarian%2C+M">Mohammad Bavarian</a>, <a href="/search/cs?searchtype=author&query=Belgum%2C+J">Jeff Belgum</a>, <a href="/search/cs?searchtype=author&query=Bello%2C+I">Irwan Bello</a>, <a href="/search/cs?searchtype=author&query=Berdine%2C+J">Jake Berdine</a>, <a href="/search/cs?searchtype=author&query=Bernadett-Shapiro%2C+G">Gabriel Bernadett-Shapiro</a>, <a href="/search/cs?searchtype=author&query=Berner%2C+C">Christopher Berner</a>, <a href="/search/cs?searchtype=author&query=Bogdonoff%2C+L">Lenny Bogdonoff</a>, <a href="/search/cs?searchtype=author&query=Boiko%2C+O">Oleg Boiko</a> , et al. (256 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="2303.08774v6-abstract-short" style="display: inline;"> We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based mo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.08774v6-abstract-full').style.display = 'inline'; document.getElementById('2303.08774v6-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.08774v6-abstract-full" style="display: none;"> We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.08774v6-abstract-full').style.display = 'none'; document.getElementById('2303.08774v6-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> 4 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">100 pages; updated authors list; fixed author names and added citation</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.04882">arXiv:2101.04882</a> <span> [<a href="https://arxiv.org/pdf/2101.04882">pdf</a>, <a href="https://arxiv.org/format/2101.04882">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Asymmetric self-play for automatic goal discovery in robotic manipulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=OpenAI%2C+O">OpenAI OpenAI</a>, <a href="/search/cs?searchtype=author&query=Plappert%2C+M">Matthias Plappert</a>, <a href="/search/cs?searchtype=author&query=Sampedro%2C+R">Raul Sampedro</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+T">Tao Xu</a>, <a href="/search/cs?searchtype=author&query=Akkaya%2C+I">Ilge Akkaya</a>, <a href="/search/cs?searchtype=author&query=Kosaraju%2C+V">Vineet Kosaraju</a>, <a href="/search/cs?searchtype=author&query=Welinder%2C+P">Peter Welinder</a>, <a href="/search/cs?searchtype=author&query=D%27Sa%2C+R">Ruben D'Sa</a>, <a href="/search/cs?searchtype=author&query=Petron%2C+A">Arthur Petron</a>, <a href="/search/cs?searchtype=author&query=Pinto%2C+H+P+d+O">Henrique P. d. O. Pinto</a>, <a href="/search/cs?searchtype=author&query=Paino%2C+A">Alex Paino</a>, <a href="/search/cs?searchtype=author&query=Noh%2C+H">Hyeonwoo Noh</a>, <a href="/search/cs?searchtype=author&query=Weng%2C+L">Lilian Weng</a>, <a href="/search/cs?searchtype=author&query=Yuan%2C+Q">Qiming Yuan</a>, <a href="/search/cs?searchtype=author&query=Chu%2C+C">Casey Chu</a>, <a href="/search/cs?searchtype=author&query=Zaremba%2C+W">Wojciech Zaremba</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="2101.04882v1-abstract-short" style="display: inline;"> We train a single, goal-conditioned policy that can solve many robotic manipulation tasks, including tasks with previously unseen goals and objects. We rely on asymmetric self-play for goal discovery, where two agents, Alice and Bob, play a game. Alice is asked to propose challenging goals and Bob aims to solve them. We show that this method can discover highly diverse and complex goals without an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.04882v1-abstract-full').style.display = 'inline'; document.getElementById('2101.04882v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.04882v1-abstract-full" style="display: none;"> We train a single, goal-conditioned policy that can solve many robotic manipulation tasks, including tasks with previously unseen goals and objects. We rely on asymmetric self-play for goal discovery, where two agents, Alice and Bob, play a game. Alice is asked to propose challenging goals and Bob aims to solve them. We show that this method can discover highly diverse and complex goals without any human priors. Bob can be trained with only sparse rewards, because the interaction between Alice and Bob results in a natural curriculum and Bob can learn from Alice's trajectory when relabeled as a goal-conditioned demonstration. Finally, our method scales, resulting in a single policy that can generalize to many unseen tasks such as setting a table, stacking blocks, and solving simple puzzles. Videos of a learned policy is available at https://robotics-self-play.github.io. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.04882v1-abstract-full').style.display = 'none'; document.getElementById('2101.04882v1-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> 13 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 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">Videos are shown at https://robotics-self-play.github.io</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1912.06680">arXiv:1912.06680</a> <span> [<a href="https://arxiv.org/pdf/1912.06680">pdf</a>, <a href="https://arxiv.org/format/1912.06680">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Dota 2 with Large Scale Deep Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=OpenAI"> OpenAI</a>, <a href="/search/cs?searchtype=author&query=%3A"> :</a>, <a href="/search/cs?searchtype=author&query=Berner%2C+C">Christopher Berner</a>, <a href="/search/cs?searchtype=author&query=Brockman%2C+G">Greg Brockman</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+B">Brooke Chan</a>, <a href="/search/cs?searchtype=author&query=Cheung%2C+V">Vicki Cheung</a>, <a href="/search/cs?searchtype=author&query=D%C4%99biak%2C+P">Przemys艂aw D臋biak</a>, <a href="/search/cs?searchtype=author&query=Dennison%2C+C">Christy Dennison</a>, <a href="/search/cs?searchtype=author&query=Farhi%2C+D">David Farhi</a>, <a href="/search/cs?searchtype=author&query=Fischer%2C+Q">Quirin Fischer</a>, <a href="/search/cs?searchtype=author&query=Hashme%2C+S">Shariq Hashme</a>, <a href="/search/cs?searchtype=author&query=Hesse%2C+C">Chris Hesse</a>, <a href="/search/cs?searchtype=author&query=J%C3%B3zefowicz%2C+R">Rafal J贸zefowicz</a>, <a href="/search/cs?searchtype=author&query=Gray%2C+S">Scott Gray</a>, <a href="/search/cs?searchtype=author&query=Olsson%2C+C">Catherine Olsson</a>, <a href="/search/cs?searchtype=author&query=Pachocki%2C+J">Jakub Pachocki</a>, <a href="/search/cs?searchtype=author&query=Petrov%2C+M">Michael Petrov</a>, <a href="/search/cs?searchtype=author&query=Pinto%2C+H+P+d+O">Henrique P. d. O. Pinto</a>, <a href="/search/cs?searchtype=author&query=Raiman%2C+J">Jonathan Raiman</a>, <a href="/search/cs?searchtype=author&query=Salimans%2C+T">Tim Salimans</a>, <a href="/search/cs?searchtype=author&query=Schlatter%2C+J">Jeremy Schlatter</a>, <a href="/search/cs?searchtype=author&query=Schneider%2C+J">Jonas Schneider</a>, <a href="/search/cs?searchtype=author&query=Sidor%2C+S">Szymon Sidor</a>, <a href="/search/cs?searchtype=author&query=Sutskever%2C+I">Ilya Sutskever</a>, <a href="/search/cs?searchtype=author&query=Tang%2C+J">Jie Tang</a> , et al. (2 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="1912.06680v1-abstract-short" style="display: inline;"> On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learnin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.06680v1-abstract-full').style.display = 'inline'; document.getElementById('1912.06680v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1912.06680v1-abstract-full" style="display: none;"> On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.06680v1-abstract-full').style.display = 'none'; document.getElementById('1912.06680v1-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> 13 December, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.04554">arXiv:1911.04554</a> <span> [<a href="https://arxiv.org/pdf/1911.04554">pdf</a>, <a href="https://arxiv.org/format/1911.04554">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Geometry-Aware Neural Rendering </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tobin%2C+J">Josh Tobin</a>, <a href="/search/cs?searchtype=author&query=Robotics%2C+O">OpenAI Robotics</a>, <a href="/search/cs?searchtype=author&query=Abbeel%2C+P">Pieter Abbeel</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="1911.04554v1-abstract-short" style="display: inline;"> Understanding the 3-dimensional structure of the world is a core challenge in computer vision and robotics. Neural rendering approaches learn an implicit 3D model by predicting what a camera would see from an arbitrary viewpoint. We extend existing neural rendering to more complex, higher dimensional scenes than previously possible. We propose Epipolar Cross Attention (ECA), an attention mechanism… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.04554v1-abstract-full').style.display = 'inline'; document.getElementById('1911.04554v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.04554v1-abstract-full" style="display: none;"> Understanding the 3-dimensional structure of the world is a core challenge in computer vision and robotics. Neural rendering approaches learn an implicit 3D model by predicting what a camera would see from an arbitrary viewpoint. We extend existing neural rendering to more complex, higher dimensional scenes than previously possible. We propose Epipolar Cross Attention (ECA), an attention mechanism that leverages the geometry of the scene to perform efficient non-local operations, requiring only $O(n)$ comparisons per spatial dimension instead of $O(n^2)$. We introduce three new simulated datasets inspired by real-world robotics and demonstrate that ECA significantly improves the quantitative and qualitative performance of Generative Query Networks (GQN). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.04554v1-abstract-full').style.display = 'none'; document.getElementById('1911.04554v1-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> 27 October, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </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">16 pages, 13 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1910.07113">arXiv:1910.07113</a> <span> [<a href="https://arxiv.org/pdf/1910.07113">pdf</a>, <a href="https://arxiv.org/format/1910.07113">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey 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">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Solving Rubik's Cube with a Robot Hand </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=OpenAI"> OpenAI</a>, <a href="/search/cs?searchtype=author&query=Akkaya%2C+I">Ilge Akkaya</a>, <a href="/search/cs?searchtype=author&query=Andrychowicz%2C+M">Marcin Andrychowicz</a>, <a href="/search/cs?searchtype=author&query=Chociej%2C+M">Maciek Chociej</a>, <a href="/search/cs?searchtype=author&query=Litwin%2C+M">Mateusz Litwin</a>, <a href="/search/cs?searchtype=author&query=McGrew%2C+B">Bob McGrew</a>, <a href="/search/cs?searchtype=author&query=Petron%2C+A">Arthur Petron</a>, <a href="/search/cs?searchtype=author&query=Paino%2C+A">Alex Paino</a>, <a href="/search/cs?searchtype=author&query=Plappert%2C+M">Matthias Plappert</a>, <a href="/search/cs?searchtype=author&query=Powell%2C+G">Glenn Powell</a>, <a href="/search/cs?searchtype=author&query=Ribas%2C+R">Raphael Ribas</a>, <a href="/search/cs?searchtype=author&query=Schneider%2C+J">Jonas Schneider</a>, <a href="/search/cs?searchtype=author&query=Tezak%2C+N">Nikolas Tezak</a>, <a href="/search/cs?searchtype=author&query=Tworek%2C+J">Jerry Tworek</a>, <a href="/search/cs?searchtype=author&query=Welinder%2C+P">Peter Welinder</a>, <a href="/search/cs?searchtype=author&query=Weng%2C+L">Lilian Weng</a>, <a href="/search/cs?searchtype=author&query=Yuan%2C+Q">Qiming Yuan</a>, <a href="/search/cs?searchtype=author&query=Zaremba%2C+W">Wojciech Zaremba</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+L">Lei Zhang</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="1910.07113v1-abstract-short" style="display: inline;"> We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. This is made possible by two key components: a novel algorithm, which we call automatic domain randomization (ADR) and a robot platform built for machine learning. ADR automatically generates a distribution over randomized environments of ever-increasing di… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.07113v1-abstract-full').style.display = 'inline'; document.getElementById('1910.07113v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1910.07113v1-abstract-full" style="display: none;"> We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. This is made possible by two key components: a novel algorithm, which we call automatic domain randomization (ADR) and a robot platform built for machine learning. ADR automatically generates a distribution over randomized environments of ever-increasing difficulty. Control policies and vision state estimators trained with ADR exhibit vastly improved sim2real transfer. For control policies, memory-augmented models trained on an ADR-generated distribution of environments show clear signs of emergent meta-learning at test time. The combination of ADR with our custom robot platform allows us to solve a Rubik's cube with a humanoid robot hand, which involves both control and state estimation problems. Videos summarizing our results are available: https://openai.com/blog/solving-rubiks-cube/ <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.07113v1-abstract-full').style.display = 'none'; document.getElementById('1910.07113v1-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> 15 October, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1812.06162">arXiv:1812.06162</a> <span> [<a href="https://arxiv.org/pdf/1812.06162">pdf</a>, <a href="https://arxiv.org/format/1812.06162">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> An Empirical Model of Large-Batch Training </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=McCandlish%2C+S">Sam McCandlish</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+J">Jared Kaplan</a>, <a href="/search/cs?searchtype=author&query=Amodei%2C+D">Dario Amodei</a>, <a href="/search/cs?searchtype=author&query=Team%2C+O+D">OpenAI Dota Team</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="1812.06162v1-abstract-short" style="display: inline;"> In an increasing number of domains it has been demonstrated that deep learning models can be trained using relatively large batch sizes without sacrificing data efficiency. However the limits of this massive data parallelism seem to differ from domain to domain, ranging from batches of tens of thousands in ImageNet to batches of millions in RL agents that play the game Dota 2. To our knowledge the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.06162v1-abstract-full').style.display = 'inline'; document.getElementById('1812.06162v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1812.06162v1-abstract-full" style="display: none;"> In an increasing number of domains it has been demonstrated that deep learning models can be trained using relatively large batch sizes without sacrificing data efficiency. However the limits of this massive data parallelism seem to differ from domain to domain, ranging from batches of tens of thousands in ImageNet to batches of millions in RL agents that play the game Dota 2. To our knowledge there is limited conceptual understanding of why these limits to batch size differ or how we might choose the correct batch size in a new domain. In this paper, we demonstrate that a simple and easy-to-measure statistic called the gradient noise scale predicts the largest useful batch size across many domains and applications, including a number of supervised learning datasets (MNIST, SVHN, CIFAR-10, ImageNet, Billion Word), reinforcement learning domains (Atari and Dota), and even generative model training (autoencoders on SVHN). We find that the noise scale increases as the loss decreases over a training run and depends on the model size primarily through improved model performance. Our empirically-motivated theory also describes the tradeoff between compute-efficiency and time-efficiency, and provides a rough model of the benefits of adaptive batch-size training. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.06162v1-abstract-full').style.display = 'none'; document.getElementById('1812.06162v1-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> 14 December, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1808.00177">arXiv:1808.00177</a> <span> [<a href="https://arxiv.org/pdf/1808.00177">pdf</a>, <a href="https://arxiv.org/format/1808.00177">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey 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">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Learning Dexterous In-Hand Manipulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=OpenAI"> OpenAI</a>, <a href="/search/cs?searchtype=author&query=Andrychowicz%2C+M">Marcin Andrychowicz</a>, <a href="/search/cs?searchtype=author&query=Baker%2C+B">Bowen Baker</a>, <a href="/search/cs?searchtype=author&query=Chociej%2C+M">Maciek Chociej</a>, <a href="/search/cs?searchtype=author&query=Jozefowicz%2C+R">Rafal Jozefowicz</a>, <a href="/search/cs?searchtype=author&query=McGrew%2C+B">Bob McGrew</a>, <a href="/search/cs?searchtype=author&query=Pachocki%2C+J">Jakub Pachocki</a>, <a href="/search/cs?searchtype=author&query=Petron%2C+A">Arthur Petron</a>, <a href="/search/cs?searchtype=author&query=Plappert%2C+M">Matthias Plappert</a>, <a href="/search/cs?searchtype=author&query=Powell%2C+G">Glenn Powell</a>, <a href="/search/cs?searchtype=author&query=Ray%2C+A">Alex Ray</a>, <a href="/search/cs?searchtype=author&query=Schneider%2C+J">Jonas Schneider</a>, <a href="/search/cs?searchtype=author&query=Sidor%2C+S">Szymon Sidor</a>, <a href="/search/cs?searchtype=author&query=Tobin%2C+J">Josh Tobin</a>, <a href="/search/cs?searchtype=author&query=Welinder%2C+P">Peter Welinder</a>, <a href="/search/cs?searchtype=author&query=Weng%2C+L">Lilian Weng</a>, <a href="/search/cs?searchtype=author&query=Zaremba%2C+W">Wojciech Zaremba</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="1808.00177v5-abstract-short" style="display: inline;"> We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system like friction coefficients and an object's appearance. Our policies transfer to the physical robot despite… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.00177v5-abstract-full').style.display = 'inline'; document.getElementById('1808.00177v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1808.00177v5-abstract-full" style="display: none;"> We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system like friction coefficients and an object's appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five. We also include a video of our results: https://youtu.be/jwSbzNHGflM <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.00177v5-abstract-full').style.display = 'none'; document.getElementById('1808.00177v5-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> 18 January, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 August, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2018. </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">Making OpenAI the first author. We wish this paper to be cited as "Learning Dexterous In-Hand Manipulation" by OpenAI et al. We are replicating the approach from the physics community: arXiv:1812.06489</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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