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pro vyhledávání: '"Wagener, Nolan"'
Autor:
Jiang, Zhengyao, Xu, Yingchen, Wagener, Nolan, Luo, Yicheng, Janner, Michael, Grefenstette, Edward, Rocktäschel, Tim, Tian, Yuandong
Humanoid control is an important research challenge offering avenues for integration into human-centric infrastructures and enabling physics-driven humanoid animations. The daunting challenges in this field stem from the difficulty of optimizing in h
Externí odkaz:
http://arxiv.org/abs/2312.02682
Autor:
Meng, Xiangyun, Hatch, Nathan, Lambert, Alexander, Li, Anqi, Wagener, Nolan, Schmittle, Matthew, Lee, JoonHo, Yuan, Wentao, Chen, Zoey, Deng, Samuel, Okopal, Greg, Fox, Dieter, Boots, Byron, Shaban, Amirreza
Effective use of camera-based vision systems is essential for robust performance in autonomous off-road driving, particularly in the high-speed regime. Despite success in structured, on-road settings, current end-to-end approaches for scene predictio
Externí odkaz:
http://arxiv.org/abs/2303.15771
Autor:
Wagener, Nolan, Kolobov, Andrey, Frujeri, Felipe Vieira, Loynd, Ricky, Cheng, Ching-An, Hausknecht, Matthew
Simulated humanoids are an appealing research domain due to their physical capabilities. Nonetheless, they are also challenging to control, as a policy must drive an unstable, discontinuous, and high-dimensional physical system. One widely studied ap
Externí odkaz:
http://arxiv.org/abs/2208.07363
Autor:
Hausknecht, Matthew, Wagener, Nolan
Dropout has long been a staple of supervised learning, but is rarely used in reinforcement learning. We analyze why naive application of dropout is problematic for policy-gradient learning algorithms and introduce consistent dropout, a simple techniq
Externí odkaz:
http://arxiv.org/abs/2202.11818
Many sequential decision problems involve finding a policy that maximizes total reward while obeying safety constraints. Although much recent research has focused on the development of safe reinforcement learning (RL) algorithms that produce a safe p
Externí odkaz:
http://arxiv.org/abs/2106.09110
Model predictive control (MPC) is a powerful technique for solving dynamic control tasks. In this paper, we show that there exists a close connection between MPC and online learning, an abstract theoretical framework for analyzing online decision mak
Externí odkaz:
http://arxiv.org/abs/1902.08967
Imitation learning (IL) consists of a set of tools that leverage expert demonstrations to quickly learn policies. However, if the expert is suboptimal, IL can yield policies with inferior performance compared to reinforcement learning (RL). In this p
Externí odkaz:
http://arxiv.org/abs/1805.10413
Publikováno v:
S. Levine, N. Wagener, P. Abbeel, "Learning Contact-Rich Manipulation Skills with Guided Policy Search," in International Conference on Robotics and Automation (ICRA), 2015
Autonomous learning of object manipulation skills can enable robots to acquire rich behavioral repertoires that scale to the variety of objects found in the real world. However, current motion skill learning methods typically restrict the behavior to
Externí odkaz:
http://arxiv.org/abs/1501.05611
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