Tobias Egle
Papers
3
Total Citations
13
H-Index
3
About
Tobias Egle is a robotics researcher specializing in bipedal locomotion and humanoid robot control. His work focuses on enhancing the robustness and versatility of walking and running in humanoid robots, particularly under dynamic and unpredictable conditions. Egle’s major contributions include the development of analytical trajectory generation frameworks for continuous gait transitions between walking and running, leveraging the Divergent Component of Motion (DCM) algorithm. He has also pioneered reinforcement learning-based methods for step timing and region adaptation, enabling robots to recover from strong pushes and external disturbances. His research on online DCM trajectory generation integrates model predictive control (MPC) for real-time step and timing adaptation, significantly improving stability during double support phases. With key papers published in 2022–2024, his work has garnered early citations, reflecting its growing impact in the field. Egle’s achievements include advancing humanoid locomotion toward more human-like agility and resilience, with potential applications in disaster response, assistive robotics, and dynamic environments. His innovative integration of analytical and learning-based approaches positions him as a notable contributor to modern legged robotics.
Research Focus
Key Achievements
Top Papers
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