Giovanni Franzese — accepted AI-conference papers

2026

ILeSiA: Interactive Learning of Robot Situational Awareness from Camera Input

Petr Vanc, Giovanni Franzese, Jan Kristof Behrens, Cosimo Della Santina, Karla Stepanova, Jens Kober +1

ICRA 2026poster

Learning from demonstration is a promising way to teach robots new skills. However, a central challenge in executing acquired skills is the ability to recognize faults and prevent failures. This is essential since the demonstrations usually cover only a limited number of mostly successful cases. Dur…

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2026

Real-Time Generation of Near-Minimum-Energy Trajectories Via Constraint-Informed Residual Learning

Domenico Dona', Giovanni Franzese, Cosimo Della Santina, Paolo Boscariol, Basilio Lenzo

ICRA 2026poster

Industrial robotics demands significant energy to operate, making energy-reduction methodologies increasingly important. Strategies for planning minimum-energy trajectories typically involve solving nonlinear optimal control problems (OCPs), which rarely cope with real-time requirements. In this pap…

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2024

A Unifying Variational Framework for Gaussian Process Motion Planning

Lucas C. Cosier, Rares Iordan, Sicelukwanda N. T. Zwane, Giovanni Franzese, James T. Wilson, Marc Deisenroth +2

AISTATS 2024poster

To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints, generating smooth and stable motions, avoiding obstacles, and preventing collisions. A motion planning algorithm must t…

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2024

Learning Multi-Reference Frame Skills from Demonstration with Task-Parameterized Gaussian Processes

Mariano Ramírez Montero, Giovanni Franzese, Jens Kober, Cosimo Della Santina

IROS 2024poster

A central challenge in Learning from Demonstration is to generate representations that are adaptable and can generalize to unseen situations. This work proposes to learn such a representation without using task-specific heuristics within the context of multi-reference frame skill learning by superim…

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2022

Learning to Pick at Non-Zero-Velocity From Interactive Demonstrations

Anna Mészáros, Giovanni Franzese, Jens Kober

RA-L 2022

This work investigates how the intricate task of a continuous pick & place (P&P) motion may be learned from humans based on demonstrations and corrections. Due to the complexity of the task, these demonstrations are often slow and even slightly flawed, particularly at moments when multiple aspects (

Cited by 21 Source ↗code ↗Scholar ↗

2021

ILoSA: Interactive Learning of Stiffness and Attractors

Giovanni Franzese, Anna Mészáros, Luka Peternel, Jens Kober

IROS 2021poster

Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives. This paper studies how to learn variable impedance policies where both the Cartesian stiffness and the attractor can be learned from human demons…

Cited by 36 Source ↗code ↗Scholar ↗

2020

Learning Interactively to Resolve Ambiguity in Reference Frame Selection

Giovanni Franzese, Carlos Celemin, Jens Kober

CoRL 2020

In Learning from Demonstrations, ambiguities can lead to bad generalization of the learned policy. This paper proposes a framework called Learning Interactively to Resolve Ambiguity (LIRA), that recognizes ambiguous situations, in which more than one action have similar probabilities, avoids a rando

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