Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Kyle Hollins Wray"'
Autor:
Kyle Hollins Wray, Kenneth Czuprynski
Publikováno v:
2022 International Conference on Robotics and Automation (ICRA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::17ad18295a156401ae0d4dfbd7e9d585
https://doi.org/10.1109/icra46639.2022.9812262
https://doi.org/10.1109/icra46639.2022.9812262
Multi-objective optimization models that encode ordered sequential constraints provide a solution to model various challenging problems including encoding preferences, modeling a curriculum, and enforcing measures of safety. A recently developed theo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2a69a5e204793290938af952d0f80a30
Publikováno v:
2021 IEEE Intelligent Vehicles Symposium (IV).
We present a solution for intelligent planning of engine activations for series hybrid electric vehicles (HEVs), Beyond minimizing energy expenditure, other real-world objectives must be incorporated, such as minimizing the perceived engine noise and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::336ce556835c340776755782ce53ccf0
https://doi.org/10.1109/iv48863.2021.9575308
https://doi.org/10.1109/iv48863.2021.9575308
Autor:
Managing Editor, Aaron Adler, Prithviraj Dasgupta, Nick DePalma, Mohammed Eslami, Richard Freedman, John Laird, Christian Lebiere, Katrin Lohan, Ross Mead, Mark Roberts, Paul Rosenbloom, Emmanuel Senft, Frank Stein, Tom Williams, Kyle Hollins Wray, Fusun Yaman, Shlomo Zilberstein
Publikováno v:
AI Magazine. 40:66-72
The AAAI 2018 Fall Symposium Series was held Thursday through Saturday, October 18–20, at the Westin Arlington Gateway in Arlington, Virginia, adjacent to Washington, D.C. The titles of the eight symposia were Adversary-Aware Learning Techniques an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c19f2dbf836f64311165e09741bb66b0
https://doi.org/10.1609/aimag.v40i2.2887
https://doi.org/10.1609/aimag.v40i2.2887
Autor:
Kenneth Czuprynski, Kyle Hollins Wray
Publikováno v:
ICRA
This paper presents a novel policy representation for partially observable Markov decision processes (POMDPs) called circulant controllers and a provably efficient gradient-based algorithm for them. A formal mathematical description is provided that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7a952be5fc5533a9f7e6e254a2187465
https://doi.org/10.1109/icra48506.2021.9561478
https://doi.org/10.1109/icra48506.2021.9561478
Autor:
Arec Jamgochian, Bernard Lange, David Ilstrup, Stefan Witwicki, Kyle Hollins Wray, Sachin Hagaribommanahalli, Atsuhide Kobashi
Publikováno v:
ISR
We present solutions for autonomous vehicles in limited visibility scenarios, such as traversing T-intersections, as well as detail how these scenarios can be handled simultaneously. The approach models each problem separately as a partially observab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::827ef988e3351acb5b300fec9299a41e
https://doi.org/10.1109/isr50024.2021.9419519
https://doi.org/10.1109/isr50024.2021.9419519
Robots deployed in the real world over extended periods of time need to reason about unexpected failures, learn to predict them, and to proactively take actions to avoid future failures. Existing approaches for competence-aware planning are either mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::762b4f79a927c4cc385e3a0c8105d7fc
Publikováno v:
AREA@ECAI
Given the complexity of real-world, unstructured domains, it is often impossible or impractical to design models that include every feature needed to handle all possible scenarios that an autonomous system may encounter. For an autonomous system to b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74ae01baee8e6763c054fd0a35d5bc79
Publikováno v:
IROS
Due to the complexity of the real world, autonomous systems use decision-making models that rely on simplifying assumptions to make them computationally tractable and feasible to design. However, since these limited representations cannot fully captu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c8e2edf1c7285cf534f0ef419f4e934e
https://doi.org/10.1109/iros40897.2019.8967676
https://doi.org/10.1109/iros40897.2019.8967676
Publikováno v:
IROS
We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem -- a general framework to model path planning and decision making in stochastic environments with goal uncertainty. The framework extends the stochastic shortest path (SSP) model
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3515d8cffbeb321f1f83b474bb784b37
https://doi.org/10.1109/iros40897.2019.8967718
https://doi.org/10.1109/iros40897.2019.8967718