Alexandre Antunes
University of Lisbon, University of Plymouth, Italian Institute of Technology
Papers
6
Total Citations
63
H-Index
5
About
Alexandre Antunes is a researcher at the intersection of robotics, natural language processing, and cognitive architectures, dedicated to enabling robots to understand and execute human instructions. His key contributions lie in grounding language in robot actions, using recurrent neural networks to bridge the gap between verbal commands and motor tasks. His most influential work, "From human instructions to robot actions: Formulation of goals, affordances and probabilistic planning" (35 citations), tackles the challenge of mapping spoken language—often ambiguous or economical—to precise robot behaviors. Antunes further advanced this field with his "Bi-directional Multiple Timescales LSTM Model" (9 citations), which learns a two-way mapping between actions and verbs, and explored bidirectional task learning with the MTRNN architecture (5 citations). His work also extends to healthcare robotics, notably the "HR1 Robot: An Assistant for Healthcare Applications" (6 citations), addressing the growing need for robotic support in aging populations. Inspired by neuropsychology and developmental psychology, Antunes has proposed architectures that mimic human executive functions, planning, and memory. With a cumulative impact spanning over 60 citations, his research is paving the way for more intuitive, language-driven human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1
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- 3HR1 Robot: An Assistant for Healthcare Applications6 citations · 2022
- 4
- 5Solving Bidirectional Tasks using MTRNN5 citations · 2018
- 6