Search Results - "Scholz, Jon"

A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning by Vecerik, Mel, Sushkov, Oleg, Barker, David, Rothorl, Thomas, Hester, Todd, Scholz, Jon

“…Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based…”
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Offline Meta-Reinforcement Learning for Industrial Insertion by Zhao, Tony Z., Luo, Jianlan, Sushkov, Oleg, Pevceviciute, Rugile, Heess, Nicolas, Scholz, Jon, Schaal, Stefan, Levine, Sergey

“…Reinforcement learning (RL) can in principle let robots automatically adapt to new tasks, but current RL methods require a large number of trials to accomplish…”
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Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings by Vecerik, Mel, Kay, Jackie, Hadsell, Raia, Agapito, Lourdes, Scholz, Jon

“…Dense object tracking, the ability to localize specific object points with pixel-level accuracy, is an important computer vision task with numerous downstream…”
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Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings by Vecerik, Mel, Kay, Jackie, Hadsell, Raia, Agapito, Lourdes, Scholz, Jon

“…Dense object tracking, the ability to localize specific object points with pixel-level accuracy, is an important computer vision task with numerous downstream…”
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RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation by Vecerik, Mel, Doersch, Carl, Yang, Yi, Davchev, Todor, Aytar, Yusuf, Zhou, Guangyao, Hadsell, Raia, Agapito, Lourdes, Scholz, Jon

“…For robots to be useful outside labs and specialized factories we need a way to teach them new useful behaviors quickly. Current approaches lack either the…”
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Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation by Sharma, Mohit, Fantacci, Claudio, Zhou, Yuxiang, Koppula, Skanda, Heess, Nicolas, Scholz, Jon, Aytar, Yusuf

“…Recent works have shown that large models pretrained on common visual learning tasks can provide useful representations for a wide range of specialized…”
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RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation by Vecerik, Mel, Doersch, Carl, Yang, Yi, Davchev, Todor, Aytar, Yusuf, Zhou, Guangyao, Hadsell, Raia, Agapito, Lourdes, Scholz, Jon

“…For robots to be useful outside labs and specialized factories we need a way to teach them new useful behaviors quickly. Current approaches lack either the…”
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DemoStart: Demonstration-led auto-curriculum applied to sim-to-real with multi-fingered robots by Bauza, Maria, Chen, Jose Enrique, Dalibard, Valentin, Gileadi, Nimrod, Hafner, Roland, Martins, Murilo F, Moore, Joss, Pevceviciute, Rugile, Laurens, Antoine, Rao, Dushyant, Zambelli, Martina, Riedmiller, Martin, Scholz, Jon, Bousmalis, Konstantinos, Nori, Francesco, Heess, Nicolas

“…We present DemoStart, a novel auto-curriculum reinforcement learning method capable of learning complex manipulation behaviors on an arm equipped with a…”
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Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation by Davchev, Todor, Sushkov, Oleg, Regli, Jean-Baptiste, Schaal, Stefan, Aytar, Yusuf, Wulfmeier, Markus, Scholz, Jon

“…International Conference on Learning Representations (ICLR 2022) Complex sequential tasks in continuous-control settings often require agents to successfully…”
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Offline Meta-Reinforcement Learning for Industrial Insertion by Zhao, Tony Z, Luo, Jianlan, Sushkov, Oleg, Pevceviciute, Rugile, Heess, Nicolas, Scholz, Jon, Schaal, Stefan, Levine, Sergey

“…Reinforcement learning (RL) can in principle let robots automatically adapt to new tasks, but current RL methods require a large number of trials to accomplish…”
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Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study by Luo, Jianlan, Sushkov, Oleg, Pevceviciute, Rugile, Lian, Wenzhao, Su, Chang, Vecerik, Mel, Ye, Ning, Schaal, Stefan, Scholz, Jon

“…Over the past several years there has been a considerable research investment into learning-based approaches to industrial assembly, but despite significant…”
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RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation by Bousmalis, Konstantinos, Vezzani, Giulia, Rao, Dushyant, Devin, Coline, Lee, Alex X, Bauza, Maria, Davchev, Todor, Zhou, Yuxiang, Gupta, Agrim, Raju, Akhil, Laurens, Antoine, Fantacci, Claudio, Dalibard, Valentin, Zambelli, Martina, Martins, Murilo, Pevceviciute, Rugile, Blokzijl, Michiel, Denil, Misha, Batchelor, Nathan, Lampe, Thomas, Parisotto, Emilio, Żołna, Konrad, Reed, Scott, Colmenarejo, Sergio Gómez, Scholz, Jon, Abdolmaleki, Abbas, Groth, Oliver, Regli, Jean-Baptiste, Sushkov, Oleg, Rothörl, Tom, Chen, José Enrique, Aytar, Yusuf, Barker, Dave, Ortiz, Joy, Riedmiller, Martin, Springenberg, Jost Tobias, Hadsell, Raia, Nori, Francesco, Heess, Nicolas

“…The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to…”
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A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning by Vecerik, Mel, Sushkov, Oleg, Barker, David, Rothörl, Thomas, Hester, Todd, Scholz, Jon

“…Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based…”
Get full text