Guilherme Maeda

About

Guilherme Maeda is a prominent robotics researcher whose work sits at the intersection of machine learning, human-robot interaction, and motion planning. His research has fundamentally advanced how robots learn from human demonstrations and collaborate fluidly with human partners, with a particular focus on probabilistic movement primitives — a powerful framework for encoding, adapting, and generalizing robot skills. Maeda's most influential contribution, "Probabilistic Movement Primitives for Coordination of Multiple Human-Robot Collaborative Tasks" (2016, 192 citations), established a foundational approach for enabling robots to anticipate and synchronize with human actions during shared tasks. Building on this, his sequence of papers on Interaction Primitives and collaborative learning (2014–2015, collectively nearly 200 citations) demonstrated how robots can adapt to diverse human partners through imitation and demonstration-based learning. Beyond collaboration, Maeda has made notable contributions to ergonomic human-robot interaction, movement segmentation and library construction, active learning, and high-speed dynamic tasks such as robotic table tennis. His work consistently bridges theoretical rigor with practical application, making robots safer, more adaptable, and more intuitive collaborators. With over 700 cumulative citations across his key publications, Maeda's research has become essential reading for anyone working in robot learning and human-robot collaboration.

Research Focus

Key Achievements

Top Papers

1. 1
   Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks

   192 citations · 2016
2. 2
   Learning multiple collaborative tasks with a mixture of Interaction Primitives

   96 citations · 2015
3. 3
   Learning interaction for collaborative tasks with probabilistic movement primitives

   92 citations · 2014
4. 4
   Phase estimation for fast action recognition and trajectory generation in human–robot collaboration

   81 citations · 2017
5. 5
   Postural optimization for an ergonomic human-robot interaction

   80 citations · 2017
6. 6
   Learning movement primitive libraries through probabilistic segmentation

   67 citations · 2017
7. 7
   Online optimal trajectory generation for robot table tennis

   59 citations · 2018
8. 8
   Demonstration based trajectory optimization for generalizable robot motions

   51 citations · 2016
9. 9
   Probabilistic segmentation applied to an assembly task

   39 citations · 2015
10. 10
    Active Incremental Learning of Robot Movement Primitives

    38 citations · 2017

Key Collaborators

Contact & Links