(Personal Page)
AI, ML & Robotics MSc.
🇧🇷 🏳️🌈 🖖 Alola! Hi! :)
I am a Ph.D. candidate @ FEEC/Unicamp.
🔬 🕵🏻 Cognitive Architectures @ H.IAAC .
🦾 🤖 #EuFaçoRobô #EuFaçoRobôs.
Summary
Links
CST BabyBot Tutorial - ICDL-2025
Notice
This page is a work in progress, so please feel free to contact me if you have any questions or suggestions. My contact info are in the icons at the footer of the page.
News
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I Choose You, Reinforcement Learning! Trained RL Agents For Pokémon Battles
Work published in 23rd Brazilian Symposium on Games and Digital Entertainment (SBGames-2024). This paper presents an open-source benchmark of trained agents with classic RL methods and Deep Reinforcement Learning (DRL) techniques - PokeRL repository. [Read More] -
Drives and Impulses - Shaping Motivation and Procedural Learning for Humanoid Robots
Work published in 2024 IEEE International Conference on Development and Learning (ICDL). This paper addresses our initial experiments exploring the acquired behavior by a simulated humanoid agent using two different motivational systems - drives and impulses - guiding the decision making process as part of the cognitive architecture. [Read More] -
Repository - Attention Trail
Repository with modules available for an attentional system based on the selection model for perception proposed by Colombini (2016). The modules were implemented with CST. [Read More] -
Paper - A Procedural Constructive Learning Mechanism with Deep Reinforcement Learning for Cognitive Agents
Work published in 2024 at JINT Journal of Intelligent & Robotic Systems. presents a learning strategy that amalgamates deep reinforcement learning with procedural learning, mirroring the incremental learning process observed in human sensorimotor development. This approach is embedded within the CONAIM cognitive-attentional architecture, leveraging the cognitive tools of CST. [Read More]