Zobrazeno 1 - 10
of 287
pro vyhledávání: '"Heess, Nicolas"'
Autor:
Abdolmaleki, Abbas, Piot, Bilal, Shahriari, Bobak, Springenberg, Jost Tobias, Hertweck, Tim, Joshi, Rishabh, Oh, Junhyuk, Bloesch, Michael, Lampe, Thomas, Heess, Nicolas, Buchli, Jonas, Riedmiller, Martin
Existing preference optimization methods are mainly designed for directly learning from human feedback with the assumption that paired examples (preferred vs. dis-preferred) are available. In contrast, we propose a method that can leverage unpaired p
Externí odkaz:
http://arxiv.org/abs/2410.04166
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:
Khetarpal, Khimya, Guo, Zhaohan Daniel, Pires, Bernardo Avila, Tang, Yunhao, Lyle, Clare, Rowland, Mark, Heess, Nicolas, Borsa, Diana, Guez, Arthur, Dabney, Will
Learning a good representation is a crucial challenge for Reinforcement Learning (RL) agents. Self-predictive learning provides means to jointly learn a latent representation and dynamics model by bootstrapping from future latent representations (BYO
Externí odkaz:
http://arxiv.org/abs/2406.02035
Animals and robots exist in a physical world and must coordinate their bodies to achieve behavioral objectives. With recent developments in deep reinforcement learning, it is now possible for scientists and engineers to obtain sensorimotor strategies
Externí odkaz:
http://arxiv.org/abs/2405.11457
Autor:
Tirumala, Dhruva, Wulfmeier, Markus, Moran, Ben, Huang, Sandy, Humplik, Jan, Lever, Guy, Haarnoja, Tuomas, Hasenclever, Leonard, Byravan, Arunkumar, Batchelor, Nathan, Sreendra, Neil, Patel, Kushal, Gwira, Marlon, Nori, Francesco, Riedmiller, Martin, Heess, Nicolas
We apply multi-agent deep reinforcement learning (RL) to train end-to-end robot soccer policies with fully onboard computation and sensing via egocentric RGB vision. This setting reflects many challenges of real-world robotics, including active perce
Externí odkaz:
http://arxiv.org/abs/2405.02425
In order to oversee advanced AI systems, it is important to understand their underlying decision-making process. When prompted, large language models (LLMs) can provide natural language explanations or reasoning traces that sound plausible and receiv
Externí odkaz:
http://arxiv.org/abs/2404.03189
Autor:
Bruce, Jake, Dennis, Michael, Edwards, Ashley, Parker-Holder, Jack, Shi, Yuge, Hughes, Edward, Lai, Matthew, Mavalankar, Aditi, Steigerwald, Richie, Apps, Chris, Aytar, Yusuf, Bechtle, Sarah, Behbahani, Feryal, Chan, Stephanie, Heess, Nicolas, Gonzalez, Lucy, Osindero, Simon, Ozair, Sherjil, Reed, Scott, Zhang, Jingwei, Zolna, Konrad, Clune, Jeff, de Freitas, Nando, Singh, Satinder, Rocktäschel, Tim
We introduce Genie, the first generative interactive environment trained in an unsupervised manner from unlabelled Internet videos. The model can be prompted to generate an endless variety of action-controllable virtual worlds described through text,
Externí odkaz:
http://arxiv.org/abs/2402.15391
Autor:
Liang, Jacky, Xia, Fei, Yu, Wenhao, Zeng, Andy, Arenas, Montserrat Gonzalez, Attarian, Maria, Bauza, Maria, Bennice, Matthew, Bewley, Alex, Dostmohamed, Adil, Fu, Chuyuan Kelly, Gileadi, Nimrod, Giustina, Marissa, Gopalakrishnan, Keerthana, Hasenclever, Leonard, Humplik, Jan, Hsu, Jasmine, Joshi, Nikhil, Jyenis, Ben, Kew, Chase, Kirmani, Sean, Lee, Tsang-Wei Edward, Lee, Kuang-Huei, Michaely, Assaf Hurwitz, Moore, Joss, Oslund, Ken, Rao, Dushyant, Ren, Allen, Tabanpour, Baruch, Vuong, Quan, Wahid, Ayzaan, Xiao, Ted, Xu, Ying, Zhuang, Vincent, Xu, Peng, Frey, Erik, Caluwaerts, Ken, Zhang, Tingnan, Ichter, Brian, Tompson, Jonathan, Takayama, Leila, Vanhoucke, Vincent, Shafran, Izhak, Mataric, Maja, Sadigh, Dorsa, Heess, Nicolas, Rao, Kanishka, Stewart, Nik, Tan, Jie, Parada, Carolina
Large language models (LLMs) have been shown to exhibit a wide range of capabilities, such as writing robot code from language commands -- enabling non-experts to direct robot behaviors, modify them based on feedback, or compose them to perform new t
Externí odkaz:
http://arxiv.org/abs/2402.11450
Normal-form games (NFGs) are the fundamental model of strategic interaction. We study their representation using neural networks. We describe the inherent equivariance of NFGs -- any permutation of strategies describes an equivalent game -- as well a
Externí odkaz:
http://arxiv.org/abs/2402.08393
Autor:
Nasiriany, Soroush, Xia, Fei, Yu, Wenhao, Xiao, Ted, Liang, Jacky, Dasgupta, Ishita, Xie, Annie, Driess, Danny, Wahid, Ayzaan, Xu, Zhuo, Vuong, Quan, Zhang, Tingnan, Lee, Tsang-Wei Edward, Lee, Kuang-Huei, Xu, Peng, Kirmani, Sean, Zhu, Yuke, Zeng, Andy, Hausman, Karol, Heess, Nicolas, Finn, Chelsea, Levine, Sergey, Ichter, Brian
Vision language models (VLMs) have shown impressive capabilities across a variety of tasks, from logical reasoning to visual understanding. This opens the door to richer interaction with the world, for example robotic control. However, VLMs produce o
Externí odkaz:
http://arxiv.org/abs/2402.07872