Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Peter Karkus"'
Publikováno v:
Proceedings of the International Symposium on Combinatorial Search. 7:54-62
The Cooperative Path Planning (CPP) problem seeks to determine a path for a group of robots which form temporary teams to perform tasks. The multi-scale effects of simultaneously coordinating many robots distributed across the workspace while also ti
Publikováno v:
CVPR
Simultaneous localization and mapping (SLAM) remains challenging for a number of downstream applications, such as visual robot navigation, because of rapid turns, featureless walls, and poor camera quality. We introduce the Differentiable SLAM Networ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a0c974a2402c42aa696c86d1a628e83
Publikováno v:
arXiv
This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems. A DAN is composed of neural network modules, each encoding a differentiable robot algorithm and an associated model; and it is tra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::216ad766c3f6950aa720b7d00fe48c47
https://hdl.handle.net/1721.1/132313
https://hdl.handle.net/1721.1/132313
Publikováno v:
ICRA
Mapping and localization, preferably from a small number of observations, are fundamental tasks in robotics. We address these tasks by combining spatial structure (differentiable mapping) and end-to-end learning in a novel neural network architecture
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f2988a2a7a3bd8e73e787fb617bcabf
Publikováno v:
AAAI
Recurrent neural networks (RNNs) have been extraordinarily successful for prediction with sequential data. To tackle highly variable and noisy real-world data, we introduce Particle Filter Recurrent Neural Networks (PF-RNNs), a new RNN family that ex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0506a0371956f40d63c6ccc839632cd
http://arxiv.org/abs/1905.12885
http://arxiv.org/abs/1905.12885
Publikováno v:
ICRA
Scarce data is a major challenge to scaling robot learning to truly complex tasks, as we need to generalize locally learned policies over different task contexts. Contextual policy search offers data-efficient learning and generalization by explicitl
Autor:
William J. Munro, Hidetaka Nishi, Tai Tsuchizawa, Kaoru Shimizu, Koji Yamada, Nobuyuki Matsuda, Hiroki Takesue, Peter Karkus
We propose an on-chip source of entangled photon pairs that uses an arrayed-waveguide grating (AWG) with multiple nonlinear input waveguides as correlated photon pair sources. The AWG wavelength-demultiplexes photon pairs created in input waveguides
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a935b59854ac2697dabeed4d47f088ea
Autor:
Hiroki Takesue, Koji Yamada, Kaoru Shimizu, Tai Tsuchizawa, Peter Karkus, Hidetaka Nishi, William J. Munro, Nobuyuki Matsuda
Publikováno v:
Frontiers in Optics 2016.
We demonstrate the on-chip generation of path-entangled photon pairs using an arrayed-waveguide grating with nonlinear input waveguides. The scheme can be extended to the generation of high-dimensional entanglement.
Autor:
Tai Tsuchizawa, Hidetaka Nishi, Nobuyuki Matsuda, Hiroki Takesue, Koji Yamada, Peter Karkus, William J. Munro
Publikováno v:
Optics express. 22(19)
We demonstrate the generation and demultiplexing of quantum correlated photons on a monolithic photonic chip composed of silicon and silica-based waveguides. Photon pairs generated in a nonlinear silicon waveguide are successfully separated into two
Autor:
Hidetaka Nishi, Nobuyuki Matsuda, Koji Yamada, Tai Tsuchizawa, Hiroki Takesue, Peter Karkus, William J. Munro
Publikováno v:
2014 The European Conference on Optical Communication (ECOC).