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pro vyhledávání: '"Ada, Suzan Ece"'
Human brain and behavior provide a rich venue that can inspire novel control and learning methods for robotics. In an attempt to exemplify such a development by inspiring how humans acquire knowledge and transfer skills among tasks, we introduce a no
Externí odkaz:
http://arxiv.org/abs/2403.04001
Autor:
Utku, Aydin Emre, Ada, Suzan Ece, Hatipoglu, Muhammet, Derman, Mustafa, Ugur, Emre, Samur, Evren
Metabolic energy consumption of a powered lower-limb exoskeleton user mainly comes from the upper body effort since the lower body is considered to be passive. However, the upper body effort of the users is largely ignored in the literature when desi
Externí odkaz:
http://arxiv.org/abs/2402.00135
Publikováno v:
IEEE Robotics and Automation Letters 4 (2024) 3116 - 3123
Offline Reinforcement Learning (RL) methods leverage previous experiences to learn better policies than the behavior policy used for data collection. In contrast to behavior cloning, which assumes the data is collected from expert demonstrations, off
Externí odkaz:
http://arxiv.org/abs/2307.04726
Autor:
Ada, Suzan Ece, Ugur, Emre
We propose Meta-World Conditional Neural Processes (MW-CNP), a conditional world model generator that leverages sample efficiency and scalability of Conditional Neural Processes to enable an agent to sample from its own "hallucination". We intend to
Externí odkaz:
http://arxiv.org/abs/2302.10320
Autor:
Ada, Suzan Ece, Seker, M. Yunus
Sketches are abstract representations of visual perception and visuospatial construction. In this work, we proposed a new framework, Generative Adversarial Networks with Conditional Neural Movement Primitives (GAN-CNMP), that incorporates a novel adv
Externí odkaz:
http://arxiv.org/abs/2111.14934
Publikováno v:
Robotica 40 (2022) 3811-3836
Agents trained with deep reinforcement learning algorithms are capable of performing highly complex tasks including locomotion in continuous environments. We investigate transferring the learning acquired in one task to a set of previously unseen tas
Externí odkaz:
http://arxiv.org/abs/1909.01331
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Akademický článek
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Autor:
Ada, Suzan Ece
Derin pekiştirmeli öğrenme algoritmaları ile eğitilen etmenler, sürekli ortamlarda hareket dahil olmak üzere oldukça karmaşık görevleri gerçekleştirme yeteneğine sahiptir. İnsan düzeyinde bir performans elde etmek için bir görevde e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_____10208::2d844576e583c89bf08e4dd3baf941b4
https://acikbilim.yok.gov.tr/handle/20.500.12812/72343
https://acikbilim.yok.gov.tr/handle/20.500.12812/72343