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Guilherme Maeda
Preferred Networks researcher in robotics and robot motor skill learning


Eventually, our society will depend on robots as general-purpose helpers that will assist us throughout our entire lives. This vision requires robots capable of learning skills by themselves to manipulate and interact with the world in a way that is useful for us. To this end, my work involves the investigation and design of learning control methods, and their implementation and validation using real robots. 


News and upates

  1. April 2020. Our article "Phase Portraits as Movement Primitives for Fast Humanoid Robot Control" has been accepted for publication in the Neural Networks journal. [preprint]
  2. April 2020. New pre-print. "Visual Task Progress Estimation with Appearance Invariant Embeddings for Robot Control and Planning" [pdf]
  3. March 2020. We are organizing a workshop for Robotics: Science and Systems (RSS) 2020 "Closing the Academia to Real-World Gap in Service Robotics". We have a call for paper in the website. The goal is to discuss the practical (and impractical) aspects of research related to service robots, from both industry and academia.
  4. December 2019 - New NeurIPS workshop paper. Zhangwei H, Nagarajan P, Maeda G. "Swarm-Inspired Reinforcement Learning via Collaborative Inter-Agent Knowledge Distillation". In: NeurIPS 2019 Deep Reinforcement Learning Workshop. 2019. [pdf][BibTeX]
  5. December 2019 - New IJRR journal article. Lioutikov, R.; Maeda, G.; Veiga, F.; Kersting, K.; Peters, J. (2019) “Learning Attribute Grammars for Movement Primitive Sequencing”. The International Journal of Robotics Research (IJRR). In press. [BibTeX]
  6. December 2019 - I am starting to move my previous website into here. The previous one (https://gjmaeda.com/) is not going to be updated anymore. It will be eventually shut down.

Research overview