Sebastian Albrecht
Institute of Automation, Siemens (Germany), University of Freiburg
Papers
8
Total Citations
114
H-Index
5
About
Sebastian Albrecht’s research lies at the intersection of robotics, optimal control, and autonomous production, with a focus on enabling robots to move, plan, and perceive with human-like efficiency and adaptability. His most influential work, “Imitating human reaching motions using physically inspired optimization principles” (77 citations), introduced an end-to-end framework that combines markerless motion tracking with optimization to generate natural, human-like reaching motions—a foundational contribution to human-robot interaction. Albrecht has also advanced autonomous manufacturing, notably in “Bridging the Gap Between Semantics and Control for Industry 4.0 and Autonomous Production” (10 citations), where he developed algorithms that sequence and parametrize robot skills for flexible, small-lot production. His work on “Efficient Collision Modelling for Numerical Optimal Control” (2023) addresses a critical bottleneck in real-time model predictive control by ensuring collision-free motion planning. Additionally, Albrecht’s “Hierarchical Planner with Composable Action Models” (2020) tackles the complex problem of task and motion planning for multi-manipulator systems, enabling asynchronous parallelization. With a career spanning over a decade, his contributions have shaped both the theory and practice of autonomous robotic systems, earning recognition for bridging high-level semantics with low-level control.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4Efficient Collision Modelling for Numerical Optimal Control5 citations · 2023
- 5
- 6
- 7
- 8Data-Driven Synthesis of Perception Pipelines via Hierarchical Planning2 citations · 2020