Corrado Pezzato — 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

Demonstrating Adaptive Mobile Manipulation in Retail Environments

Max Spahn, Corrado Pezzato, Chadi Salmi, Rick Dekker, Cong Wang, Christian Pek +4

RSS 2024poster

Although autonomous robots have great potential to boost efficiency and throughput across the whole retail chain, they are mostly being deployed in large warehouses and distribution centers. Deploying robots in stores with customers, such as supermarkets, requires substantially more development effo…

Cited by 5 Source ↗PDF ↗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 ↗

2022

Unbiased Active Inference for Classical Control

Mohamed Baioumy, Corrado Pezzato, Riccardo Ferrari, Nick Hawes

IROS 2022poster

Active inference is a mathematical framework that originated in computational neuroscience. Recently, it has been demonstrated as a promising approach for constructing goal-driven behavior in robotics. Specifically, the active inference controller (AIC) has been successful on several continuous cont…

Cited by 3 Source ↗code ↗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 ↗