Research

My research bridges the cross-cutting disciplines of Robotics and Perception, addressing key challenges such as data scarcity, real-time processing constraints, and the need for flexible, modular systems that can learn and generalize across tasks.

Over the past 20 years, I have applied perception approaches to interdisciplinary applications in industrial process automation and robotics, including aerial, industrial, and space robotics.

Since 2020, I lead research in space robotics at SpaceR-SnT. My current research focuses on multi-modal perception approaches (vision and touch) for the autonomous operation of robots in space, especially for multi-purpose manipulation tasks for planetary and orbital robotics. It deals with fundamental and practical aspects of Machine Learning for space applications, including efforts on domain adaptation (to reduce sim2real gap), robot-environment interaction, and benchmarking (high-fidelity testing environment). I also work with gecko-inspired capturing interfaces and mechanical intelligence for secure, adaptive, and intelligent interaction with space objects.

Videos from my research can be found in my youtube channel