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Titlebook: Algorithmic Foundations of Robotics XII; Proceedings of the T Ken Goldberg,Pieter Abbeel,Lauren Miller Book 2020 Springer Nature Switzerlan

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期刊全称Algorithmic Foundations of Robotics XII
期刊简称Proceedings of the T
影响因子2023Ken Goldberg,Pieter Abbeel,Lauren Miller
视频video
发行地址Includes significant recent research on robotic algorithms.Presents edited outcomes of the 12th International Workshop on the Algorithmic Foundations of Robotics (WAFR), held on December 18–20, 2016,
学科分类Springer Proceedings in Advanced Robotics
图书封面Titlebook: Algorithmic Foundations of Robotics XII; Proceedings of the T Ken Goldberg,Pieter Abbeel,Lauren Miller Book 2020 Springer Nature Switzerlan
影响因子.This book presents the outcomes of the 12th International Workshop on the Algorithmic Foundations of Robotics (WAFR 2016). WAFR is a prestigious, single-track, biennial international meeting devoted to recent advances in algorithmic problems in robotics. Robot algorithms are an important building block of robotic systems and are used to process inputs from users and sensors, perceive and build models of the environment, plan low-level motions and high-level tasks, control robotic actuators, and coordinate actions across multiple systems. However, developing and analyzing these algorithms raises complex challenges, both theoretical and practical. Advances in the algorithmic foundations of robotics have applications to manufacturing, medicine, distributed robotics, human–robot interaction, intelligent prosthetics, computer animation, computational biology, and many other areas. ..  ..The 2016 edition of WAFR went back to its roots and was held in San Francisco, California – the city where the very first WAFR was held in 1994. Organized by Pieter Abbeel, Kostas Bekris, Ken Goldberg, and Lauren Miller, WAFR 2016 featured keynote talks by John Canny on “A Guided Tour of Computer Vision
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Cloud-based Motion Plan Computation for Power-Constrained Robots, robot the ability to adapt to changes between updates from the server. In our results, we show that with typical latency and bandwidth limitations, our method gains significant improvement in the responsiveness and quality of motion plans in interactive scenarios.
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Generating Plans that Predict Themselves,ng robot plan from the overall task goal and the observed initial . actions in the plan. We contribute a method for generating .-predictable plans: we search for a full plan that accomplishes the task, but in which the first . actions make it as easy as possible to infer the remaining ones. The resu
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,You Can’t Save all the Pandas: Impossibility Results for Privacy-Preserving Tracking, to the information available to the robot at the start — conditions we call initial I-state dependent cases. Quite naturally in the one-dimensional model, one may quantify sensing power by the number of perceptual (or output) classes available to the robot. The number of initial I-state dependent c
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Approximation Algorithms for Tours of Height-varying View Cones,n agriculture application. We further study its performance in simulation using randomly generated cone sets. Our results indicate that the performance of our algorithm is superior to standard solutions.
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Reactive Motion Planning in Uncertain Environments via Mutual Information Policies, on mutual information, returns a complete policy and a bound on the gap between the policy’s expected cost and the optimal. Finally, simulations are run on a flexible factory scenario to demonstrate the scalability of the proposed approach.
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