Alisson Henrique Kolling
Universidade Federal de Santa Maria, Universidade Federal do Rio Grande
Papers
14
Total Citations
211
H-Index
6
About
Alisson Henrique Kolling is a robotics researcher whose work sits at the intersection of deep reinforcement learning and autonomous robot navigation. His research primarily focuses on developing mapless navigation algorithms for a diverse range of robotic platforms, including terrestrial mobile robots, unmanned aerial vehicles (UAVs), and novel hybrid aerial-underwater vehicles — a particularly ambitious domain that demands robust decision-making across radically different physical environments. Kolling's most influential contribution, "Soft Actor-Critic for Navigation of Mobile Robots" (2021), has garnered 93 citations, establishing him as a notable voice in data-efficient, model-free reinforcement learning for robotics. His follow-up work on double critic architectures and distributional deep reinforcement learning techniques further refines navigation performance under uncertainty, collectively accumulating over 70 additional citations. Beyond navigation, Kolling has made meaningful contributions to human-robot social interaction, developing simulation frameworks like Jubileo and virtual reality platforms that accelerate safe and cost-effective robot design and testing. His progression toward image-based navigation using high-dimensional inputs signals an evolving research agenda tackling increasingly complex real-world challenges. For students interested in autonomous systems, reinforcement learning, or multi-domain robotics, Kolling's body of work offers both foundational methodologies and cutting-edge applications worth studying closely.
Research Focus
Key Achievements
Top Papers
- 1Soft Actor-Critic for Navigation of Mobile Robots93 citations · 2021
- 2
- 3Jubileo: An Immersive Simulation Framework for Social Robot Design18 citations · 2023
- 4
- 5
- 6
- 7
- 8
- 9
- 10