Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Erez, Tom"'
Autor:
Tracy, Kevin, Manchester, Zachary, Jain, Ajinkya, Go, Keegan, Schaal, Stefan, Erez, Tom, Tassa, Yuval
Contact-rich manipulation tasks with stiff frictional elements like connector insertion are difficult to model with rigid-body simulators. In this work, we propose a new approach for modeling these environments by learning a quasi-static contact forc
Externí odkaz:
http://arxiv.org/abs/2312.09190
Autor:
Yu, Wenhao, Gileadi, Nimrod, Fu, Chuyuan, Kirmani, Sean, Lee, Kuang-Huei, Arenas, Montse Gonzalez, Chiang, Hao-Tien Lewis, Erez, Tom, Hasenclever, Leonard, Humplik, Jan, Ichter, Brian, Xiao, Ted, Xu, Peng, Zeng, Andy, Zhang, Tingnan, Heess, Nicolas, Sadigh, Dorsa, Tan, Jie, Tassa, Yuval, Xia, Fei
Large language models (LLMs) have demonstrated exciting progress in acquiring diverse new capabilities through in-context learning, ranging from logical reasoning to code-writing. Robotics researchers have also explored using LLMs to advance the capa
Externí odkaz:
http://arxiv.org/abs/2306.08647
Autor:
Howell, Taylor, Gileadi, Nimrod, Tunyasuvunakool, Saran, Zakka, Kevin, Erez, Tom, Tassa, Yuval
We introduce MuJoCo MPC (MJPC), an open-source, interactive application and software framework for real-time predictive control, based on MuJoCo physics. MJPC allows the user to easily author and solve complex robotics tasks, and currently supports t
Externí odkaz:
http://arxiv.org/abs/2212.00541
Autor:
Ortega, Pedro A., Kunesch, Markus, Delétang, Grégoire, Genewein, Tim, Grau-Moya, Jordi, Veness, Joel, Buchli, Jonas, Degrave, Jonas, Piot, Bilal, Perolat, Julien, Everitt, Tom, Tallec, Corentin, Parisotto, Emilio, Erez, Tom, Chen, Yutian, Reed, Scott, Hutter, Marcus, de Freitas, Nando, Legg, Shane
The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domains. One important problem class that has remained relatively elusive h
Externí odkaz:
http://arxiv.org/abs/2110.10819
Autor:
Tassa, Yuval, Tunyasuvunakool, Saran, Muldal, Alistair, Doron, Yotam, Trochim, Piotr, Liu, Siqi, Bohez, Steven, Merel, Josh, Erez, Tom, Lillicrap, Timothy, Heess, Nicolas
The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation. A MuJoCo wrapper provides convenient bindings to functions and data structures. The PyMJCF and Co
Externí odkaz:
http://arxiv.org/abs/2006.12983
Autor:
Merel, Josh, Tunyasuvunakool, Saran, Ahuja, Arun, Tassa, Yuval, Hasenclever, Leonard, Pham, Vu, Erez, Tom, Wayne, Greg, Heess, Nicolas
We address the longstanding challenge of producing flexible, realistic humanoid character controllers that can perform diverse whole-body tasks involving object interactions. This challenge is central to a variety of fields, from graphics and animati
Externí odkaz:
http://arxiv.org/abs/1911.06636
Autor:
Jeong, Rae, Kay, Jackie, Romano, Francesco, Lampe, Thomas, Rothorl, Tom, Abdolmaleki, Abbas, Erez, Tom, Tassa, Yuval, Nori, Francesco
Learning robotic control policies in the real world gives rise to challenges in data efficiency, safety, and controlling the initial condition of the system. On the other hand, simulations are a useful alternative as they provide an abundant source o
Externí odkaz:
http://arxiv.org/abs/1910.09471
Autor:
Uesato, Jonathan, Kumar, Ananya, Szepesvari, Csaba, Erez, Tom, Ruderman, Avraham, Anderson, Keith, Krishmamurthy, Dvijotham, Heess, Nicolas, Kohli, Pushmeet
This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. We focus on two problems: searching for scenarios when learned agents fail and a
Externí odkaz:
http://arxiv.org/abs/1812.01647
Autor:
Amos, Brandon, Dinh, Laurent, Cabi, Serkan, Rothörl, Thomas, Colmenarejo, Sergio Gómez, Muldal, Alistair, Erez, Tom, Tassa, Yuval, de Freitas, Nando, Denil, Misha
We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world. We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world.
Externí odkaz:
http://arxiv.org/abs/1804.06318
Autor:
Zhu, Yuke, Wang, Ziyu, Merel, Josh, Rusu, Andrei, Erez, Tom, Cabi, Serkan, Tunyasuvunakool, Saran, Kramár, János, Hadsell, Raia, de Freitas, Nando, Heess, Nicolas
We propose a model-free deep reinforcement learning method that leverages a small amount of demonstration data to assist a reinforcement learning agent. We apply this approach to robotic manipulation tasks and train end-to-end visuomotor policies tha
Externí odkaz:
http://arxiv.org/abs/1802.09564