Larissa Casteluci
Papers
1
Total Citations
2
H-Index
1
About
Larissa Casteluci is a researcher at the intersection of robotics and artificial intelligence, with a primary focus on deep reinforcement learning for autonomous navigation and control. Her most notable contribution is the development of a Deep Reinforcement Learning agent for a four-wheeled rover operating in a challenging, multi-goal competition task. In her key work, she advanced the state of the art by integrating both visual and dynamics sensory inputs, enabling the rover to successfully navigate despite noisy GPS measurements—a significant step beyond prior implementations that relied solely on raw dynamics. This research, published in 2019, has garnered 2 citations and demonstrates her ability to tackle real-world sensor uncertainty in autonomous systems. Casteluci’s work is particularly relevant for students and researchers interested in applying reinforcement learning to physical robots, as it highlights the critical role of multi-modal sensing in robust control. Her contributions provide a foundation for further exploration into how autonomous agents can adapt to imperfect environmental data, making her a promising voice in the field of intelligent robotics.
Research Focus
Key Achievements
Top Papers
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