Jair Augusto Bottega
Papers
10
Total Citations
133
H-Index
6
About
Jair Augusto Bottega is a robotics and artificial intelligence researcher whose work sits at the intersection of deep reinforcement learning, mobile robot navigation, and human-robot social interaction. He is best known for pioneering applications of Deep Deterministic Policy Gradient (DDPG) algorithms to autonomous mobile robot navigation, work that has collectively garnered nearly 70 citations and established him as a notable voice in mapless navigation research. His contributions demonstrate how low-dimensional sensory inputs — such as laser range findings and velocity data — can be leveraged to train robots to navigate complex environments without relying on pre-built maps. Beyond navigation, Bottega has made meaningful strides in social robotics, developing the Jubileo platform, an open-source robot and immersive simulation framework designed to advance human-robot interaction research. His more recent work reflects a forward-thinking curiosity, exploring cutting-edge architectures such as Kolmogorov-Arnold Networks for online reinforcement learning and high-fidelity procedural simulators integrating Unity3D with ROS. With over 130 cumulative citations across his publication record, Bottega's research consistently bridges theoretical innovation with practical robotics applications, making his body of work particularly valuable for students and researchers entering the fields of autonomous systems and social robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Kolmogorov-Arnold Networks for Online Reinforcement Learning19 citations · 2024
- 3Deep Deterministic Policy Gradient for Navigation of Mobile Robots18 citations · 2020
- 4Jubileo: An Immersive Simulation Framework for Social Robot Design18 citations · 2023
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