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您的检索式：作者名="Aleksandra Faust"
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| 1 | Continuous Action Reinforcement Learning for Control-Affine Systems with Unknown Dynamics显示文摘Control of nonlinear systems is challenging in realtime.Decision making,performed many times per second,must ensure system safety.Designing input to perform a task often involves solving a nonlinear system of differential equations,which is a computationally intensive,if not intractable problem.This article proposes sampling-based task learning for controlaffine nonlinear systems through the combined learning of both state and action-value functions in a model-free approximate value iteration setting with continuous inputs.A quadratic negative definite state-value function implies the existence of a unique maximum of the action-value function at any state.This allows the replacement of the standard greedy policy with a computationally efficient policy approximation that guarantees progression to a goal state without knowledge of the system dynamics.The policy approximation is consistent,i.e.,it does not depend on the action samples used to calculate it.This method is appropriate for mechanical systems with high-dimensional input spaces and unknown dynamics performing Constraint-Balancing Tasks.We verify it both in simulation and experimentally for an Unmanned Aerial Vehicles(UAVs) carrying a suspended load,and in simulation,for the rendezvous of heterogeneous robots. | Aleksandra Faust Peter Ruymgaart Molly Salman Rafael Fierro Lydia Tapia | 2014 | IEEE/CAA Journal of Automatica Sinica2014，1，3： | 3 |
| 2 | Google提出FollowNet提高机器人导航能力显示文摘在未知的环境中,人们经常通过观察周围的环境,并遵循指令进行导航。而这些指令主要由地标和方向指示性指令以及其他常用词语组成。最近,Google将类似于人类的指令遵循应用到机器人在二维工作空间中的导航任务中,为智能体提供指令,并对其进行训练以遵循指令。为了进行有效导航,Google提出了Follow Net。它是一个用于学习多模态导航策略的端到端的可微神经架构,可提高智能体在环境中的导航能力。 | Pararth Shah Marek Fiser Aleksandra Faust J.Chase Kew Dilek Hakkani-Tur | 2018 | 机器人产业2018，，3： | 0 |
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