Daniel T. Larsson
Papers
2
Total Citations
22
H-Index
2
About
Daniel T. Larsson is a pioneering researcher at the intersection of robotics, human-machine interaction, and autonomous perception. His work primarily focuses on Human Swarm Interaction (HSI) and Brain Machine Interfaces (BMI), where he explores intuitive control paradigms for complex robotic swarms. His highly cited 2017 paper, "A hybrid BMI for control of robotic swarms: Preliminary results," has garnered 20 citations and laid foundational groundwork for making swarm control more accessible to human operators. More recently, Larsson has advanced autonomous robotics through his 2023 work on "Information-theoretic Abstraction of Semantic Octree Models for Integrated Perception and Planning." This paper introduces a novel method for building and compressing three-dimensional semantic environment representations from raw sensor data, enabling robots to efficiently perceive and plan in complex spaces. By integrating information theory with semantic tree structures, Larsson’s approach bridges the gap between perception and planning, offering a scalable solution for autonomous systems. His contributions are shaping how robots understand and interact with their environments, making him a notable figure in modern robotics research.
Research Focus
Key Achievements
Top Papers
- 1A hybrid BMI for control of robotic swarms: Preliminary results20 citations · 2017
- 2