Angelo Bratta
Papers
11
Total Citations
113
H-Index
6
About
Angelo Bratta is a robotics researcher whose work sits at the intersection of motion planning, optimization, and autonomous systems, with a particular focus on legged locomotion. He is best known for advancing Nonlinear Model Predictive Control (NMPC) techniques that enable quadruped robots to navigate complex, unstructured terrain dynamically and robustly. His 2021 paper on environment-adaptive MPC for legged locomotion has garnered 46 citations, establishing him as a notable voice in the field. Bratta has made significant contributions to computational efficiency in robot control, including a distributed optimization framework using ADMM to accelerate MPC in real time, and a principled foothold evaluation criterion for maintaining balance during dynamic transitions. His optimization-based reference generator further reduces reliance on hand-tuned heuristics in legged robot planning. Beyond locomotion, his research extends into agricultural robotics — developing vision-based pruning systems for grapevines — environmental robotics through the litter-collecting quadruped VERO, and robust point cloud registration. This breadth reflects a researcher driven by real-world impact, translating theoretical advances in optimization and perception into practical autonomous systems across diverse and challenging application domains.
Research Focus
Key Achievements
Top Papers
- 1Model Predictive Control With Environment Adaptation for Legged Locomotion46 citations · 2021
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- 8Mobility-enhanced MPC for Legged Locomotion on Rough Terrain.4 citations · 2021
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