Carlos Hernández Corbato — accepted AI-conference papers

2025

Sampling-Based Model Predictive Control Leveraging Parallelizable Physics Simulations

Corrado Pezzato, Chadi Salmi, Elia Trevisan, Max Spahn, Javier Alonso-Mora, Carlos Hernández Corbato

RA-L 2025

We present a sampling-based model predictive control method that uses a generic physics simulator as the dynamical model. In particular, we propose a Model Predictive Path Integral controller (MPPI) that employs the GPU-parallelizable IsaacGym simulator to compute the forward dynamics of the robot a

Cited by 15 Source ↗code ↗Scholar ↗

2024

Multi-Modal MPPI and Active Inference for Reactive Task and Motion Planning

Yuezhe Zhang, Corrado Pezzato, Elia Trevisan, Chadi Salmi, Carlos Hernández Corbato, Javier Alonso-Mora

RA-L 2024

Task and Motion Planning (TAMP) has made strides in complex manipulation tasks, yet the execution robustness of the planned solutions remains overlooked. In this work, we propose a method for reactive TAMP to cope with runtime uncertainties and disturbances. We combine an Active Inference planner (A

Cited by 20 Source ↗Scholar ↗

2020

A Novel Adaptive Controller for Robot Manipulators Based on Active Inference

Corrado Pezzato, Riccardo M. G. Ferrari, Carlos Hernández Corbato

RA-L 2020

More adaptive controllers for robot manipulators are needed, which can deal with large model uncertainties. This letter presents a novel active inference controller (AIC) as an adaptive control scheme for industrial robots. This scheme is easily scalable to high degrees-of-freedom, and it maintains

Cited by 94 Source ↗Scholar ↗