Lukas Mack

Max Planck Institute for Intelligent Systems, University of Augsburg

Papers

Total Citations

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About

Lukas Mack is a robotics researcher specializing in state estimation and multimodal perception for dynamic locomotion and dexterous manipulation. His work focuses on fusing visual-inertial data with leg odometry to enable reliable robot posture estimation during challenging motions, such as flight phases in dynamic legged locomotion—a domain where traditional methods often fail. His most-cited paper (2023, 9 citations) introduces a novel fusion approach that significantly improves base height estimation, a critical factor for stable and agile robotic movement. Mack also explores visuo-tactile pose estimation for multi-finger robot hands, addressing the problem of object occlusion during grasping. His 2025 paper (3 citations) proposes combining low-resolution tactile sensing with visual data to achieve accurate 3D object pose estimation, a key enabler for assembly and in-hand manipulation tasks. By bridging perception and control in both legged and manipulative robotics, Mack’s contributions advance the robustness and autonomy of robots operating in complex, real-world environments.