G
Guilherme Maeda
Researcher at Technische Universität Darmstadt
Publications - 56
Citations - 1279
Guilherme Maeda is an academic researcher from Technische Universität Darmstadt. The author has contributed to research in topics: Robot & Robotic arm. The author has an hindex of 18, co-authored 52 publications receiving 965 citations. Previous affiliations of Guilherme Maeda include Tokyo Institute of Technology & University of Sydney.
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Journal ArticleDOI
Probabilistic movement primitives for coordination of multiple human---robot collaborative tasks
TL;DR: An interaction learning method for collaborative and assistive robots based on movement primitives that allows for both action recognition and human–robot movement coordination and is scalable in relation to the number of tasks.
Proceedings ArticleDOI
Learning multiple collaborative tasks with a mixture of Interaction Primitives
TL;DR: A Mixture of Interaction Primitives is proposed to learn multiple interaction patterns from unlabeled demonstrations to overcome the limitation of this framework to represent and generalize a single interaction pattern.
Proceedings ArticleDOI
Learning interaction for collaborative tasks with probabilistic movement primitives
TL;DR: This paper introduces the use of Probabilistic Movement Primitives (ProMPs) to devise an interaction method that both recognizes the action of a human and generates the appropriate movement primitive of the robot assistant.
Proceedings ArticleDOI
Postural optimization for an ergonomic human-robot interaction
TL;DR: This paper uses postural assessment techniques, and a personalized human kinematic model, to optimize the model body posture to fulfill a task while avoiding uncomfortable or unsafe postures, and derives a robotic behavior that leads the worker towards that improved posture.
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Phase estimation for fast action recognition and trajectory generation in human–robot collaboration
TL;DR: The method is fundamentally different from approaches based on Dynamic Time Warping that must rely on a consistent stream of measurements at runtime, and can achieve phase estimation, action recognition and robot trajectory coordination using a single probabilistic representation.