Zobrazeno 1 - 10
of 19 287
pro vyhledávání: '"Ratliff, A."'
One of the most important yet challenging skills for a robot is the task of dexterous grasping of a diverse range of objects. Much of the prior work is limited by the speed, dexterity, or reliance on depth maps. In this paper, we introduce DextrAH-RG
Externí odkaz:
http://arxiv.org/abs/2412.01791
We propose a framework for two-player infinite-dimensional games with cooperative or competitive structure. These games take the form of coupled partial differential equations in which players optimize over a space of measures, driven by either a gra
Externí odkaz:
http://arxiv.org/abs/2411.07403
Autor:
Singh, Ritvik, Liu, Jingzhou, Van Wyk, Karl, Chao, Yu-Wei, Lafleche, Jean-Francois, Shkurti, Florian, Ratliff, Nathan, Handa, Ankur
Vision-based object detectors are a crucial basis for robotics applications as they provide valuable information about object localisation in the environment. These need to ensure high reliability in different lighting conditions, occlusions, and vis
Externí odkaz:
http://arxiv.org/abs/2410.21153
For Stokes waves in finite depth within the neighbourhood of the Benjamin-Feir stability transition, there are two families of periodic waves, one modulationally unstable and the other stable. In this paper we show that these two families can be join
Externí odkaz:
http://arxiv.org/abs/2410.17416
In recent years, in an effort to promote fairness in the election process, a wide variety of techniques and metrics have been proposed to determine whether a map is a partisan gerrymander. The most accessible measures, requiring easily obtained data,
Externí odkaz:
http://arxiv.org/abs/2409.17186
Autor:
Ratliff, Zachary, Vadhan, Salil
The standard definition of differential privacy (DP) ensures that a mechanism's output distribution on adjacent datasets is indistinguishable. However, real-world implementations of DP can, and often do, reveal information through their runtime distr
Externí odkaz:
http://arxiv.org/abs/2409.05623
Autor:
Isa, Jason T., Wu, Bohan, Wang, Qirui, Zhang, Yilin, Burden, Samuel A., Ratliff, Lillian J., Chasnov, Benjamin J.
As interactions between humans and AI become more prevalent, it is critical to have better predictors of human behavior in these interactions. We investigated how changes in the AI's adaptive algorithm impact behavior predictions in two-player contin
Externí odkaz:
http://arxiv.org/abs/2408.14640
Two methods to analyse radial diffusion ensembles: the peril of space- and time- dependent diffusion
Particle dynamics in Earth's outer radiation belt can be modelled using a diffusion framework, where large-scale electron movements are captured by a diffusion equation across a single adiabatic invariant, $L^{*}$ $``(L)"$. While ensemble models are
Externí odkaz:
http://arxiv.org/abs/2407.04669
Autor:
Lum, Tyler Ga Wei, Matak, Martin, Makoviychuk, Viktor, Handa, Ankur, Allshire, Arthur, Hermans, Tucker, Ratliff, Nathan D., Van Wyk, Karl
A pivotal challenge in robotics is achieving fast, safe, and robust dexterous grasping across a diverse range of objects, an important goal within industrial applications. However, existing methods often have very limited speed, dexterity, and genera
Externí odkaz:
http://arxiv.org/abs/2407.02274
In this paper, we study the non-asymptotic sample complexity for the pure exploration problem in contextual bandits and tabular reinforcement learning (RL): identifying an epsilon-optimal policy from a set of policies with high probability. Existing
Externí odkaz:
http://arxiv.org/abs/2406.06856