AIConfPaper
← Search

Marco Cognetti

17 accepted papers

2026

A Nonlinear MPC for Physical Human-Aerial Robot Interaction in Collaborative Transportation Tasks

Antonio Gonzalez-Morgado, Jonas Soueidan, Guillermo Heredia, Anibal Ollero, Philippe Fraisse, Marco Tognon +1
ICRA 2026poster

Aerial robots are transitioning from traditional surveillance and monitoring roles to more advanced tasks involving physical interaction. Despite this progress, physical Human-Aerial Robot Interaction remains largely underexplored due to the complexity and stability-related issues of such platforms.…

Cited by 0Source ↗Scholar ↗
2026

Contact-Robust Trajectory Planning Via Parametric Sensitivity Analysis for Hybrid Robotic Systems

Tommaso Belvedere, James Zhu, Marco Cognetti, Paolo Robuffo Giordano
ICRA 2026poster

In this paper, we combine first-order approximations of hybrid systems (i.e., the so-called saltation matrix) with previous works on parametric sensitivity for continuous systems to propose a general framework for robust trajectory optimization of hybrid systems subject to parametric uncertainties. …

Cited by 0Scholar ↗
2026

Robust Sensitivity-Aware Chance-Constrained MPC for Efficient Handling of Multiple Uncertainty Sources

James Zhu, Thierry Simeon, Marco Cognetti
ICRA 2026poster

Robust motion planning under uncertainty is a critical challenge for applications involving real-world robotic deployments. This paper introduces SupeR-MPC, a computationally-efficient, sensitivity-aware, chance-constrained optimization framework that systematically accounts for multiple sources of …

Cited by 0Source ↗Scholar ↗
2025

A Nonlinear MPC for Physical Human-Aerial Robot Interaction in Collaborative Transportation Tasks

Antonio González-Morgado, Jonas Soueidan, Guillermo Heredia, Aníbal Ollero, Philippe Fraisse, Marco Tognon +1
RA-L 2025

Aerial robots are transitioning from traditional surveillance and monitoring roles to more advanced tasks involving physical interaction. Despite this progress, physical Human-Aerial Robot Interaction remains largely underexplored due to the complexity and stability-related issues of such platforms.

Cited by 2Source ↗Scholar ↗
2025

Experimental Validation of Sensitivity-Aware Trajectory Planning for a Redundant Robotic Manipulator Under Payload Uncertainty

Ali Srour, Antonio Franchi, Paolo Robuffo Giordano, Marco Cognetti
RA-L 2025

In this letter, we experimentally validate the recent concepts of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">closed-loop state and input sensitivity</i> in the context of robust manipulation control for a robot manipulator. Our objective is to a

Cited by 5Source ↗Scholar ↗
2025

Robust Sensitivity-Aware Chance-Constrained MPC for Efficient Handling of Multiple Uncertainty Sources

James Zhu, Thierry Siméon, Marco Cognetti
RA-L 2025

Robust motion planning under uncertainty is critical for unlocking real-world robotics applications. This paper introduces SupeR-MPC, a computationally-efficient, sensitivity-aware, chance-constrained optimization framework that systematically accounts for multiple sources of uncertainty, including

Cited by 1Source ↗Scholar ↗
2024

Robust Motion Planning With Accuracy Optimization Based on Learned Sensitivity Metrics

Simon Wasiela, Marco Cognetti, Paolo Robuffo Giordano, Juan Cortés, Thierry Siméon
RA-L 2024

This letter addresses the problem of generating robust and accurate trajectories taking into account uncertainties in the robot dynamic model. Based on the notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">closed-loop sensitivity</i>, which q

Cited by 3Source ↗Scholar ↗
2024

Transformer-Based Prediction of Human Motions and Contact Forces for Physical Human-Robot Interaction

Alessia Fusco, Valerio Modugno, Dimitrios Kanoulas, Alessandro Rizzo, Marco Cognetti
ICRA 2024poster

In this paper, we propose a transformer-based architecture for predicting contact forces during a physical human-robot interaction. Our Neural Network is composed of two main parts: a Multi-Layer Perceptron called Transducer and a Transformer. The former estimates, based on the kinematic data from a…

Cited by 1Source ↗Scholar ↗
2021

Autonomy in Physical Human-Robot Interaction: A Brief Survey

Mario Selvaggio, Marco Cognetti, Stefanos Nikolaidis, Serena Ivaldi, Bruno Siciliano
RA-L 2021

Sharing the control of a robotic system with an autonomous controller allows a human to reduce his/her cognitive and physical workload during the execution of a task. In recent years, the development of inference and learning techniques has widened the spectrum of applications of shared control (SC)

Cited by 193Source ↗Scholar ↗
2020

Perception-Aware Human-Assisted Navigation of Mobile Robots on Persistent Trajectories

Marco Cognetti, Marco Aggravi, Claudio Pacchierotti, Paolo Salaris, Paolo Robuffo Giordano
RA-L 2020

We propose a novel shared control and active perception framework combining the skills of a human operator in accomplishing complex tasks with the capabilities of a mobile robot in autonomously maximizing the information acquired by the onboard sensors for improving its state estimation. The human o

Cited by 40Source ↗Scholar ↗
2019

Dynamic Identification of the Franka Emika Panda Robot With Retrieval of Feasible Parameters Using Penalty-Based Optimization

Claudio Gaz, Marco Cognetti, Alexander Antonio Oliva, Paolo Robuffo Giordano, Alessandro De Luca
RA-L 2019

In this letter, we address the problem of extracting a feasible set of dynamic parameters characterizing the dynamics of a robot manipulator. We start by identifying through an ordinary least squares approach the dynamic coefficients that linearly parametrize the model. From these, we retrieve a set

Cited by 260Source ↗Scholar ↗
2017

Real-time pursuit-evasion with humanoid robots

Marco Cognetti, Daniele De Simone, Federico Patota, Nicola Scianca, Leonardo Lanari, Giuseppe Oriolo
ICRA 2017poster

We consider a pursuit-evasion problem between humanoids. In our scenario, the pursuer enters the safety area of the evader headed for collision, while the latter executes a fast evasive motion. Control schemes are designed for both the pursuer and the evader. They are structurally identical, althoug…

Cited by 15Source ↗Scholar ↗
2016

Real-time planning and execution of evasive motions for a humanoid robot

Marco Cognetti, Daniele De Simone, Leonardo Lanari, Giuseppe Oriolo
ICRA 2016

We present a method for performing evasive motions with a humanoid robot. In the considered scenario, the robot is standing in a workspace, when a moving obstacle (e.g., a human, or another robot) enters its safety area and heads towards it; the humanoid must plan and execute in real-time a maneuver

Cited by 8Source ↗Scholar ↗
2016

Rearrangement planning using object-centric and robot-centric action spaces

Jennifer E. King, Marco Cognetti, Siddhartha S. Srinivasa
ICRA 2016

This paper addresses the problem of rearrangement planning, i.e. to find a feasible trajectory for a robot that must interact with multiple objects in order to achieve a goal. We propose a planner to solve the rearrangement planning problem by considering two different types of actions: robot-centri

Cited by 95Source ↗Scholar ↗