About

Michael Posa is a robotics researcher whose work sits at the intersection of trajectory optimization, contact mechanics, and legged locomotion. He is perhaps best known for pioneering direct methods for trajectory optimization through contact—work that fundamentally advanced how robots plan motions involving impacts, such as walking, running, and manipulation. His 2013 paper on this topic has accumulated over 600 citations, establishing him as a leading voice in contact-rich robot planning. Posa has also made significant contributions to stabilization and stability analysis of rigid-body systems with friction and impacts, applying Lyapunov-based and sums-of-squares techniques to formally certify robot balance and fall recovery. His research extends into model reduction for bipedal locomotion, seeking principled ways to derive simplified yet accurate planning models. A 2023 survey on optimization-based control for dynamic legged robots, already garnering 160 citations, reflects his broad influence on the field's trajectory toward agile quadrupeds, bipeds, and humanoids. Early in his career, Posa also contributed to high-profile demonstrations through the DARPA Robotics Challenge. Across these diverse contributions, his work consistently bridges theoretical rigor with practical robotic systems, making him a highly impactful figure in modern robot locomotion and control research.

Research Focus

Key Achievements

14
H-Index
33
Papers
1,615
Total Citations
49
Avg Citations/Paper
🏆 Most Cited Paper
A direct method for trajectory optimization of rigid bodies through contact
608 citations · 2013
📈 Most Prolific Year: 2024 (7 Papers)
🤝 Key Collaborators: 50
🏛 Institutions: Massachusetts Institute of Technology, University of Pennsylvania, Vassar College, California University of Pennsylvania

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago