Asia La Rocca — accepted AI-conference papers

2024

Receding-Constraint Model Predictive Control using a Learned Approximate Control-Invariant Set

Gianni Lunardi, Asia La Rocca, Matteo Saveriano, Andrea Del Prete

ICRA 2024poster

In recent years, advanced model-based and data-driven control methods are unlocking the potential of complex robotics systems, and we can expect this trend to continue at an exponential rate in the near future. However, ensuring safety with these advanced control methods remains a challenge. A well-…

Cited by 2 Source ↗code ↗Scholar ↗

2023

VBOC: Learning the Viability Boundary of a Robot Manipulator Using Optimal Control

Asia La Rocca, Matteo Saveriano, Andrea Del Prete

RA-L 2023

Safety is often the most important requirement in robotics applications. Nonetheless, control techniques that can provide safety guarantees are still extremely rare for nonlinear systems, such as robot manipulators. A well-known tool to ensure safety is the viability kernel, which is the largest set

Cited by 6 Source ↗Scholar ↗