Semantics and Learning for Active Robot Perception in Dynamic Environments

Abstract: The ability to autonomously explore and model an unknown and changing environment is a fundamental capability for robot autonomy, and a prerequisite for numerous applications in industrial, construction, household, service, and assistive robotics. This talk explores how various forms of scene understanding, ranging from traditional geometry, end-to-end learning, semantic perception, and abstraction, can enable robots to actively reconstruct an unknown environment, detect and understand dynamic entities, and leverage prediction and adaptation for improved task performance in changing scenes. The presented methods are validated running on-board fully autonomous robots and the code is released as open source.

Speaker bio: Lukas Schmid is a postdoctoral fellow working with Luca Carlone at the MIT-SPARK Lab. Before that, he briefly was a postdoctoral researcher at the Autonomous Systems Lab lead by Prof. Roland Siegwart at ETH Zürich, Switzerland, where he also obtained his PhD in 2022 and M.Sc. in Robotics, Systems, and Control in 2019. Among others, his work was honored with the Willi Studer Prize for the best M.Sc. graduate, the ETH Medal for outstanding master theses, and a Swiss National Science Foundation postdoctoral fellowship.