Papers
2
Total Citations
6
H-Index
2
About
Matteo Ciocca’s research centers on bipedal locomotion and control, with a particular focus on enabling robots to navigate complex, dynamic environments. His major contributions lie at the intersection of Model Predictive Control (MPC) and impedance optimization for legged systems. In his most cited work, Ciocca developed an MPC-based navigation framework that allows a biped robot to move stably through a crowd by automatically planning footstep placements and re-planning walking motions in real time—overcoming the limitations of traditional footstep-by-footstep strategies. This work has garnered 4 citations and addresses a critical challenge in human-robot interaction. Additionally, Ciocca tackled the problem of stable footholds on uneven terrain by analytically computing optimal ankle impedances, modeling the foot-leg system as a free-floating two-body system to ensure safe, robust foot planting. This contribution, with 2 citations, provides a principled foundation for fast locomotion over rough ground. Through these efforts, Ciocca has advanced the practical deployment of bipedal robots in unstructured, unpredictable settings—a key step toward robots that can walk alongside humans in the real world.
Research Focus
Key Achievements
Top Papers
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- 2