John Rogers
Papers
2
Total Citations
28
H-Index
2
About
John Rogers is a researcher at the forefront of multi-robot perception and human-robot interaction, with a focus on enabling autonomous systems to navigate complex, unstructured environments. His most influential work, "Multi Robot Object-Based SLAM" (2017, 26 citations), introduced a pioneering framework for collaborative simultaneous localization and mapping, where teams of robots leverage object-level landmarks to build shared, semantically rich maps. This contribution has been foundational for scalable, robust multi-agent exploration. More recently, Rogers has advanced the field of learning from demonstration, as seen in his 2021 paper on "Risk Averse Bayesian Reward Learning for Autonomous Navigation from Human Demonstration." This work addresses a critical gap in imitation learning by incorporating risk sensitivity into Bayesian reward inference, allowing non-expert users to safely and intuitively teach robots navigation policies. By bridging probabilistic reasoning with human-guided learning, Rogers’ research is shaping the next generation of adaptable, human-aware autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Multi Robot Object-Based SLAM26 citations · 2017
- 2