Michael Levashov

Artificial Intelligence in Medicine (Canada), Massachusetts Institute of Technology

Papers

Total Citations

142

H-Index

About

Michael Levashov is a roboticist whose research centers on dynamic locomotion and motion planning for legged robots, with a particular focus on enabling stable, agile movement across challenging terrain. His most influential work, "Bounding on rough terrain with the LittleDog robot" (2010, 126 citations), introduced a novel motion planning algorithm that addressed the fundamental challenge of controlling highly dynamic bounding gaits—a stark contrast to slower, quasi-static walking. By modeling LittleDog as a planar five-link system with a 16-dimensional state space, Levashov demonstrated how to compute feasible trajectories for rapid, robust locomotion over uneven ground, advancing the frontier of legged robotics. He also contributed to the analysis of hybrid limit cycles in walking robots, exploring regions of attraction to ensure stability in periodic gaits. Levashov's work has been foundational for researchers developing agile robots capable of navigating real-world obstacles, and his citation record reflects the lasting impact of his contributions to dynamic legged locomotion and motion planning.