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pro vyhledávání: '"Coninx, P"'
Quality-Diversity (QD) methods are algorithms that aim to generate a set of diverse and high-performing solutions to a given problem. Originally developed for evolutionary robotics, most QD studies are conducted on a limited set of domains - mainly a
Externí odkaz:
http://arxiv.org/abs/2308.05483
Autor:
Mira Meeus, Wim Marneffe, Julie Sylvie van Eetvelde, Annick A A Timmermans, Karin Coninx, Kristof Kempeneers, Timo Meus, Iris Meuwissen, Nathalie Anne Roussel, Gaetane Stassijns, Jonas Verbrugghe
Publikováno v:
BMJ Open Sport & Exercise Medicine, Vol 10, Iss 4 (2024)
Chronic low back pain (CLBP) is one of the most common chronic musculoskeletal disorders worldwide. Guidelines recommend exercise therapy (ET) in CLBP management, but more research is needed to investigate specific ET modalities and their underlying
Externí odkaz:
https://doaj.org/article/52256d9de9224860988285eefb226969
This paper studies the impact of the initial data gathering method on the subsequent learning of a dynamics model. Dynamics models approximate the true transition function of a given task, in order to perform policy search directly on the model rathe
Externí odkaz:
http://arxiv.org/abs/2210.11801
Robotics grasping refers to the task of making a robotic system pick an object by applying forces and torques on its surface. Despite the recent advances in data-driven approaches, grasping remains an unsolved problem. Most of the works on this task
Externí odkaz:
http://arxiv.org/abs/2210.07887
Grasping a particular object may require a dedicated grasping movement that may also be specific to the robot end-effector. No generic and autonomous method does exist to generate these movements without making hypotheses on the robot or on the objec
Externí odkaz:
http://arxiv.org/abs/2205.08189
Publikováno v:
GECCO 22 Companion, July 9-13, 2022, Boston, MA, USA
The Novelty Search (NS) algorithm was proposed more than a decade ago. However, the mechanisms behind its empirical success are still not well formalized/understood. This short note focuses on the effects of the archive on exploration. Experimental e
Externí odkaz:
http://arxiv.org/abs/2205.03162
Learning optimal policies in sparse rewards settings is difficult as the learning agent has little to no feedback on the quality of its actions. In these situations, a good strategy is to focus on exploration, hopefully leading to the discovery of a
Externí odkaz:
http://arxiv.org/abs/2111.01919
Publikováno v:
Frontiers in Robotics and AI, 14 February 2022
Not having access to compact and meaningful representations is known to significantly increase the complexity of reinforcement learning (RL). For this reason, it can be useful to perform state representation learning (SRL) before tackling RL tasks. H
Externí odkaz:
http://arxiv.org/abs/2109.13596
Publikováno v:
IEEE Robotics and Automation Letters 7.2 (2022): 4424-4431
In the past few years, a considerable amount of research has been dedicated to the exploitation of previous learning experiences and the design of Few-shot and Meta Learning approaches, in problem domains ranging from Computer Vision to Reinforcement
Externí odkaz:
http://arxiv.org/abs/2109.06826
As open-ended learning based on divergent search algorithms such as Novelty Search (NS) draws more and more attention from the research community, it is natural to expect that its application to increasingly complex real-world problems will require t
Externí odkaz:
http://arxiv.org/abs/2104.03936