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About

Ruben Grandia is a robotics researcher whose work sits at the intersection of legged locomotion, optimal control, and robot perception. He has made significant contributions to advancing the mobility and autonomy of quadrupedal and wheeled-legged robots, with a particular focus on model predictive control (MPC) and whole-body optimization frameworks that enable robots to operate reliably in complex, real-world environments. Grandia's most influential work, "Perceptive Locomotion Through Nonlinear Model-Predictive Control" (2023, 237 citations), demonstrates how robots can dynamically traverse rough terrain by integrating perceptive information directly into an MPC framework — a landmark advance in bridging perception and motion planning. His broader portfolio reveals a consistent drive to unify sensing, planning, and control: from GPU-accelerated elevation mapping (105 citations) to terrain-aware trajectory optimization via TAMOLS (85 citations), and whole-body MPC for mobile manipulation (101 citations). Notably, he has also explored tactile inspection applications, including sewer infrastructure assessment and planetary soil probing using legged robots — work that extends the field's reach into practical and space exploration domains. With over 980 total citations across his top ten papers, Grandia has established himself as a leading voice in intelligent legged robotics and real-time whole-body control.