Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Tassa, Yuval"'
Autor:
D'Ambrosio, David B., Abeyruwan, Saminda, Graesser, Laura, Iscen, Atil, Amor, Heni Ben, Bewley, Alex, Reed, Barney J., Reymann, Krista, Takayama, Leila, Tassa, Yuval, Choromanski, Krzysztof, Coumans, Erwin, Jain, Deepali, Jaitly, Navdeep, Jaques, Natasha, Kataoka, Satoshi, Kuang, Yuheng, Lazic, Nevena, Mahjourian, Reza, Moore, Sherry, Oslund, Kenneth, Shankar, Anish, Sindhwani, Vikas, Vanhoucke, Vincent, Vesom, Grace, Xu, Peng, Sanketi, Pannag R.
Achieving human-level speed and performance on real world tasks is a north star for the robotics research community. This work takes a step towards that goal and presents the first learned robot agent that reaches amateur human-level performance in c
Externí odkaz:
http://arxiv.org/abs/2408.03906
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:
Haarnoja, Tuomas, Moran, Ben, Lever, Guy, Huang, Sandy H., Tirumala, Dhruva, Humplik, Jan, Wulfmeier, Markus, Tunyasuvunakool, Saran, Siegel, Noah Y., Hafner, Roland, Bloesch, Michael, Hartikainen, Kristian, Byravan, Arunkumar, Hasenclever, Leonard, Tassa, Yuval, Sadeghi, Fereshteh, Batchelor, Nathan, Casarini, Federico, Saliceti, Stefano, Game, Charles, Sreendra, Neil, Patel, Kushal, Gwira, Marlon, Huber, Andrea, Hurley, Nicole, Nori, Francesco, Hadsell, Raia, Heess, Nicolas
We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies in dynamic environments. We use
Externí odkaz:
http://arxiv.org/abs/2304.13653
Autor:
Zakka, Kevin, Wu, Philipp, Smith, Laura, Gileadi, Nimrod, Howell, Taylor, Peng, Xue Bin, Singh, Sumeet, Tassa, Yuval, Florence, Pete, Zeng, Andy, Abbeel, Pieter
Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics. Reinforcement learning is a promising approach that has achieved impressive progress in the last few years; however, the class of problems it has
Externí odkaz:
http://arxiv.org/abs/2304.04150
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:
Bohez, Steven, Tunyasuvunakool, Saran, Brakel, Philemon, Sadeghi, Fereshteh, Hasenclever, Leonard, Tassa, Yuval, Parisotto, Emilio, Humplik, Jan, Haarnoja, Tuomas, Hafner, Roland, Wulfmeier, Markus, Neunert, Michael, Moran, Ben, Siegel, Noah, Huber, Andrea, Romano, Francesco, Batchelor, Nathan, Casarini, Federico, Merel, Josh, Hadsell, Raia, Heess, Nicolas
We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots. Our approach builds upon previous work on imitating human or dog Motion Capture (MoCap) data to learn a movement skill
Externí odkaz:
http://arxiv.org/abs/2203.17138
Autor:
La Barbera, Vittorio, Pardo, Fabio, Tassa, Yuval, Daley, Monica, Richards, Christopher, Kormushev, Petar, Hutchinson, John
Muscle-actuated control is a research topic that spans multiple domains, including biomechanics, neuroscience, reinforcement learning, robotics, and graphics. This type of control is particularly challenging as bodies are often overactuated and dynam
Externí odkaz:
http://arxiv.org/abs/2112.06061
Autor:
Byravan, Arunkumar, Hasenclever, Leonard, Trochim, Piotr, Mirza, Mehdi, Ialongo, Alessandro Davide, Tassa, Yuval, Springenberg, Jost Tobias, Abdolmaleki, Abbas, Heess, Nicolas, Merel, Josh, Riedmiller, Martin
There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this intuition on various challenging locomotion tasks. We take a hybrid app
Externí odkaz:
http://arxiv.org/abs/2110.03363
Autor:
Lutter, Michael, Hasenclever, Leonard, Byravan, Arunkumar, Dulac-Arnold, Gabriel, Trochim, Piotr, Heess, Nicolas, Merel, Josh, Tassa, Yuval
Model-Based Reinforcement Learning involves learning a \textit{dynamics model} from data, and then using this model to optimise behaviour, most often with an online \textit{planner}. Much of the recent research along these lines presents a particular
Externí odkaz:
http://arxiv.org/abs/2109.14311