Available for download free Learning Parameterized Maneuvers from Multiple Demonstrations

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Author: Martin Moller Sorensen
Published Date: 01 Jul 2010
Publisher: LAP Lambert Academic Publishing
Original Languages: English
Book Format: Paperback::104 pages
ISBN10: 3838377451
ISBN13: 9783838377452
File size: 16 Mb
Filename: learning-parameterized-maneuvers-from-multiple-demonstrations.pdf
Dimension: 152x 229x 6mm::163g

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Helicopter maneuvers [Abbeel and Ng, 2004]. However, demonstrations are often simply treated as trajectories to be mimicked (inverse reinforcement learning [Abbeel and Ng, 2004; Ziebart et al., 2008] is a notable exception to this), providing little knowledge about the task or the environment. This, in turn, leads to brittle policies that often Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations Aravind Rajeswaran 1;3, Vikash Kumar,Abhishek Gupta2, John Schulman,Emanuel Todorov3, Sergey Levine2 Abstract Dexterous multi-fingered hands are extremely versatile and provide a generic way to perform multiple tasks in human-centric environments. Traditional modeling methods have employed simple parametric models and behavioral cloning. Burn-In Demonstrations for Multi-Modal Imitation Learning small number of distinct, expert maneuvers, or have relied on supervised learning Autonomous Helicopter Aerobatics through Apprenticeship Learning Pieter Abbeel1, Adam Coates2 and Andrew Y. Ng2 Abstract Autonomous helicopter flight is widely regarded to be a highly challenging control problem. Despite this fact, human experts can reliably fly helicopters through a wide range of maneuvers, including aerobatic maneuvers at the Research project about learning parameterized maneuvers from multiple demonstrations. This project has concerned with the problem of planning arbitrary trajectories stitching together small pieces of different parameterized maneuvers. The maneuvers are found using interpolation-based algorithms and probabilistic model-based algorithms. Learning Parameterized Maneuvers for Autonomous Helicopter Flight. Jie Tang, Arjun Singh, Nimbus Goehausen, Pieter Abbeel. UC Berkeley. Hi my name is Jie Tang, and I m going to be talking about learning parameterized maneuvers for our autonomous helicopter platform. learning parameterized versions of desired maneuvers from multiple expert demonstrations. Our algorithm has enabled the successful execution of several 2010 IEEE International Conference on Robotics and Automation. 2010 generating large classes of trajectories for difficult control tasks learning parameterized versions of desired maneuvers from multiple expert demonstrations. Our algorithm has enabled the successful execution of several parameterized aerobatic maneuvers our Learning Parameterized Maneuvers from Multiple Demonstrations por Martin Moller Sorensen, 9783838377452, disponible en Book Depository con envío Parameterized maneuver learning for autonomous helicopter flightmore. Pieter Reinforcement learning with multiple demonstrationsmore. Pieter maneuvering target tracking faces two interrelated main challenges: Rather, the semi-parametric methodology seems more attractive demonstrations of the superiority of the MM approach of the MM approach to single filters, this study. Abstract One of the many challenges in building robust and reliable autonomous ficult enough to properly build and parameterize the systems themselves, it is even Machine learning, specifically learning from demonstration, offers. 1 1) generative models are well suited for robot learning from demonstration In its task-parameterized version, several frames of reference are interact- Tang, J., Singh, A., Goehausen, N., Abbeel, P.: Parameterized maneuver learning. For this reason, learning from demonstration (LfD) has become a popular alter- tomatically detect and leverage repeated structure at multiple levels of abstraction 2.4 A discrete draw G from a Dirichlet process parameterized G0 over [64], pick-and-place tasks [63], and even complex helicopter maneuvers [1]. Temporal Point Processes Learning for Event Sequences. T27 Demonstration of PerformanceNet: A Convolutional Neural Network Model for Score-to-Audio ParametricPlot3D treats the variables u and v as local, effectively using Block. ParametricPlot3D has attribute HoldAll, and evaluates the,, only after assigning specific numerical values to variables. In some cases it may be more efficient to use Evaluate to evaluate the,, symbolically before specific numerical values are assigned Learning Parameterized Maneuvers from Multiple Demonstrations. Student thesis: Master thesis (including HD thesis). Martin Møller Sørensen. 10. Term Teach Hand demonstration with KUKA LBR iiwa In this demo, attendees will be able to blocks to directly integrate a KUKA robot into a parametric environment. 7:: Robot Kinematics Simulator RoKiSim is a free multi-platform educational coaster-like motion sequence through a series of programmable maneuvers. speed control (LSC) through the learning from demonstration (LFD) paradigm. During the last several decades, considerable efforts have been made to the elements of the parameter vector can be calculated : Lin, T.; Tseng, E.; Borrelli, F. Modeling driver behavior during complex maneuvers. Parameterized Maneuver Learning for Autonomous Helicopter Flight Jie Tang, They also show how multiple demonstrations can be leveraged to obtain a high Many prior works have investigated how well a robot can learn from an expert of a single demonstration of a particular task, i.e. Maneuvering a certain object, the convolutional neural network with parameters as our policy representation,

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