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- Q7353390 subject Q7015116.
- Q7353390 subject Q8670714.
- Q7353390 abstract "Robot learning is a research field at the intersection of machine learning and robotics. It studies techniques allowing a robot to acquire novel skills or adapt to its environment through learning algorithms. The embodiment of the robot, situated in a physical embedding, provides at the same time specific difficulties (e.g. high-dimensionality, real time constraints for collecting data and learning) and opportunities for guiding the learning process (e.g. sensorimotor synergies, motor primitives). Example of skills that are targeted by learning algorithms include sensorimotor skills such as locomotion, grasping, active object categorization, as well as interactive skills such as joint manipulation of an object with a human peer, and linguistic skills such as the grounded and situated meaning of human language. Learning can happen either through autonomous self-exploration or through guidance from a human teacher, like for example in robot learning by imitation.Robot learning can be closely related to adaptive control, reinforcement learning as well as developmental robotics which considers the problem of autonomous lifelong acquisition of repertoires of skills.While machine learning is frequently used by computer vision algorithms employed in the context of robotics, these applications are usually not referred to as "robot learning".".
- Q7353390 wikiPageExternalLink flowers.inria.Fr.
- Q7353390 wikiPageExternalLink humanoid.fy.chalmers.se.
- Q7353390 wikiPageExternalLink lasa.epfl.ch.
- Q7353390 wikiPageExternalLink learning-robots.de.
- Q7353390 wikiPageExternalLink robot-learning.de.
- Q7353390 wikiPageExternalLink www-clmc.usc.edu.
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- Q7353390 wikiPageExternalLink cs4758.
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- Q7353390 wikiPageExternalLink www.nimbro.net.
- Q7353390 wikiPageExternalLink www.plymouth.ac.uk.
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- Q7353390 wikiPageWikiLink Q7015116.
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- Q7353390 wikiPageWikiLink Q8670714.
- Q7353390 wikiPageWikiLink Q92735.
- Q7353390 comment "Robot learning is a research field at the intersection of machine learning and robotics. It studies techniques allowing a robot to acquire novel skills or adapt to its environment through learning algorithms. The embodiment of the robot, situated in a physical embedding, provides at the same time specific difficulties (e.g. high-dimensionality, real time constraints for collecting data and learning) and opportunities for guiding the learning process (e.g.".
- Q7353390 label "Robot learning".
- Q7353390 homepage robot-learning.