Gerardo Bledt
Massachusetts Institute of Technology, IIT@MIT, Virginia Tech
Papers
12
Total Citations
2,274
H-Index
11
About
Gerardo Bledt is a robotics researcher whose work sits at the intersection of legged locomotion, optimal control, and autonomous navigation for quadruped robots. Best known for his foundational contributions to the MIT Cheetah program, Bledt has helped shape modern approaches to dynamic robot locomotion through elegant combinations of model predictive control (MPC) and whole-body control frameworks. His 2018 papers on convex MPC-based ground reaction force optimization and the design of the MIT Cheetah 3 robot have each accumulated over 700 citations, establishing them as landmark references in the field. These works demonstrated that carefully simplified robot dynamics could enable real-time, robust locomotion control without sacrificing physical fidelity. Bledt further advanced the state of the art by developing policy-regularized MPC and regularized predictive control strategies that simultaneously optimize footstep placement and contact forces, allowing quadrupeds to transition fluidly between diverse gaits. His later research extended these capabilities to vision-aided navigation in unstructured terrains, enabling small-scale robots to operate autonomously in disaster-response scenarios. Collectively, his portfolio reflects a researcher deeply committed to bridging theoretical control design with practical, high-performance robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control717 citations · 2018
- 2MIT Cheetah 3: Design and Control of a Robust, Dynamic Quadruped Robot709 citations · 2018
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- 4Contact Model Fusion for Event-Based Locomotion in Unstructured Terrains177 citations · 2018
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- 8Extracting Legged Locomotion Heuristics with Regularized Predictive Control55 citations · 2020
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