Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Scholz, Jon"'
Autor:
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 three-fingered robotic hand, from only a sparse reward and a handful of demonstrations in simulat
Externí odkaz:
http://arxiv.org/abs/2409.06613
Autor:
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 generality to onboard new tasks without task-specific engineering, or else lack the data-effic
Externí odkaz:
http://arxiv.org/abs/2308.15975
Autor:
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 transform robot learning. Inspired by recent advances in foundation models for vision and lan
Externí odkaz:
http://arxiv.org/abs/2306.11706
Autor:
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 perception problems, as well as a variety of robotic manipulation tasks. While prior work on robot
Externí odkaz:
http://arxiv.org/abs/2304.06600
Dense object tracking, the ability to localize specific object points with pixel-level accuracy, is an important computer vision task with numerous downstream applications in robotics. Existing approaches either compute dense keypoint embeddings in a
Externí odkaz:
http://arxiv.org/abs/2112.04910
Autor:
Davchev, Todor, Sushkov, Oleg, Regli, Jean-Baptiste, Schaal, Stefan, Aytar, Yusuf, Wulfmeier, Markus, Scholz, Jon
Publikováno v:
International Conference on Learning Representations (ICLR 2022)
Complex sequential tasks in continuous-control settings often require agents to successfully traverse a set of "narrow passages" in their state space. Solving such tasks with a sparse reward in a sample-efficient manner poses a challenge to modern re
Externí odkaz:
http://arxiv.org/abs/2112.00597
Autor:
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 this. In this paper, we tackle rapid adaptation to new tasks through the framework of meta
Externí odkaz:
http://arxiv.org/abs/2110.04276
Autor:
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 progress these techniques have yet to be adopted by industry. We argue that it is the prohibi
Externí odkaz:
http://arxiv.org/abs/2103.11512
Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based robotics community can be highly effective when task geometry is known, but are complex and cumber
Externí odkaz:
http://arxiv.org/abs/1810.01531
Autor:
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 transform robot learning. Inspired by recent advances in foundation models for vision and lan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::35dd55970ae3a724788dac69eb13b5a7
http://arxiv.org/abs/2306.11706
http://arxiv.org/abs/2306.11706