About

Jonas Buchli is a pioneering robotics researcher whose work sits at the intersection of legged locomotion, optimal control, and machine learning. Best known for his transformative contributions to quadruped and humanoid robot control, Buchli has fundamentally advanced how robots move through complex, unstructured environments. His 2018 work on phase-based end-effector parameterization for gait and trajectory optimization (452 citations) introduced an elegant unified formulation that simultaneously solves for gait sequencing, footholds, and body motion without modular decomposition — a landmark achievement in locomotion planning. Equally influential is his foundational research on path integral reinforcement learning, with two seminal 2010 papers (449 and 257 citations) establishing scalable, sample-efficient frameworks for motor skill acquisition in high-dimensional spaces. His contributions to variable impedance control (329 citations) brought biologically inspired adaptability to robotic manipulation, while his inverse dynamics and contact force optimization work laid critical groundwork for compliant, physically consistent control of floating-base systems. With multiple highly cited papers on quadruped locomotion and model predictive control, Buchli's research portfolio reflects a rare breadth spanning theory and real-world implementation, making him one of the most impactful figures in modern robotics.

Research Focus

Key Achievements

42
H-Index
101
Papers
6,623
Total Citations
66
Avg Citations/Paper
🏆 Most Cited Paper
Gait and Trajectory Optimization for Legged Systems Through Phase-Based End-Effector Parameterization
452 citations · 2018
📈 Most Prolific Year: 2016 (16 Papers)
🤝 Key Collaborators: 120
🏛 Institutions: ETH Zurich, University of Southern California, Board of the Swiss Federal Institutes of Technology, Italian Institute of Technology, École Polytechnique Fédérale de Lausanne, Inspire

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 44 days ago