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pro vyhledávání: '"Whitney, A. F."'
Interrupted X-ray computed tomography (X-CT) has been the common way to observe the deformation of materials during an experiment. While this approach is effective for quasi-static experiments, it has never been possible to reconstruct a full 3d tomo
Externí odkaz:
http://arxiv.org/abs/2410.20558
Autor:
Whitney, William F., Varley, Jacob, Jain, Deepali, Choromanski, Krzysztof, Singh, Sumeet, Sindhwani, Vikas
We present High-Density Visual Particle Dynamics (HD-VPD), a learned world model that can emulate the physical dynamics of real scenes by processing massive latent point clouds containing 100K+ particles. To enable efficiency at this scale, we introd
Externí odkaz:
http://arxiv.org/abs/2406.19800
Autor:
Rubanova, Yulia, Lopez-Guevara, Tatiana, Allen, Kelsey R., Whitney, William F., Stachenfeld, Kimberly, Pfaff, Tobias
Simulating large scenes with many rigid objects is crucial for a variety of applications, such as robotics, engineering, film and video games. Rigid interactions are notoriously hard to model: small changes to the initial state or the simulation para
Externí odkaz:
http://arxiv.org/abs/2405.14045
Autor:
Lopez-Guevara, Tatiana, Rubanova, Yulia, Whitney, William F., Pfaff, Tobias, Stachenfeld, Kimberly, Allen, Kelsey R.
Accurately simulating real world object dynamics is essential for various applications such as robotics, engineering, graphics, and design. To better capture complex real dynamics such as contact and friction, learned simulators based on graph networ
Externí odkaz:
http://arxiv.org/abs/2401.11985
Autor:
Whitney, William F., Lopez-Guevara, Tatiana, Pfaff, Tobias, Rubanova, Yulia, Kipf, Thomas, Stachenfeld, Kimberly, Allen, Kelsey R.
Realistic simulation is critical for applications ranging from robotics to animation. Traditional analytic simulators sometimes struggle to capture sufficiently realistic simulation which can lead to problems including the well known "sim-to-real" ga
Externí odkaz:
http://arxiv.org/abs/2312.05359
Autor:
Pinneri, Cristina, Bechtle, Sarah, Wulfmeier, Markus, Byravan, Arunkumar, Zhang, Jingwei, Whitney, William F., Riedmiller, Martin
We present a novel approach to address the challenge of generalization in offline reinforcement learning (RL), where the agent learns from a fixed dataset without any additional interaction with the environment. Specifically, we aim to improve the ag
Externí odkaz:
http://arxiv.org/abs/2309.07578
We introduce quantile filtered imitation learning (QFIL), a novel policy improvement operator designed for offline reinforcement learning. QFIL performs policy improvement by running imitation learning on a filtered version of the offline dataset. Th
Externí odkaz:
http://arxiv.org/abs/2112.00950
Most prior approaches to offline reinforcement learning (RL) have taken an iterative actor-critic approach involving off-policy evaluation. In this paper we show that simply doing one step of constrained/regularized policy improvement using an on-pol
Externí odkaz:
http://arxiv.org/abs/2106.08909
Autor:
Whitney, William F., Bloesch, Michael, Springenberg, Jost Tobias, Abdolmaleki, Abbas, Cho, Kyunghyun, Riedmiller, Martin
Despite the close connection between exploration and sample efficiency, most state of the art reinforcement learning algorithms include no considerations for exploration beyond maximizing the entropy of the policy. In this work we address this seemin
Externí odkaz:
http://arxiv.org/abs/2101.09458
We consider the problem of evaluating representations of data for use in solving a downstream task. We propose to measure the quality of a representation by the complexity of learning a predictor on top of the representation that achieves low loss on
Externí odkaz:
http://arxiv.org/abs/2009.07368