37 citations · 67 across the 13 of their papers we have counts for
14 papers
A machine learning framework for uncovering stochastic nonlinear dynamics from noisy data
Matteo Bosso, Giovanni Franzese, Kushal Swamy +3
Modeling real-world systems requires accounting for noise - whether it arises from unpredictable fluctuations in financial markets, irregular rhythms in biological systems, or envi…
MUKCa: Accurate and Affordable Cobot Calibration Without External Measurement Devices
Giovanni Franzese, Max Spahn, Jens Kober +1
To increase the reliability of collaborative robots in performing daily tasks, we require them to be accurate and not only repeatable. However, having a calibrated kinematics model…
Real-Time Generation of Near-Minimum-Energy Trajectories via Constraint-Informed Residual Learning
Domenico Dona', Giovanni Franzese, Cosimo Della Santina +2
Industrial robotics demands significant energy to operate, making energy-reduction methodologies increasingly important. Strategies for planning minimum-energy trajectories typical…
Mastering Contact-rich Tasks by Combining Soft and Rigid Robotics with Imitation Learning
Mariano Ramírez Montero, Ebrahim Shahabi, Giovanni Franzese +3
Soft robots have the potential to revolutionize the use of robotic systems with their capability of establishing safe, robust, and adaptable interactions with their environment, bu…
ILeSiA: Interactive Learning of Robot Situational Awareness from Camera Input
Petr Vanc, Giovanni Franzese, Jan Kristof Behrens +4
Learning from demonstration is a promising approach for teaching robots new skills. However, a central challenge in the execution of acquired skills is the ability to recognize fau…
Generalizable Motion Policies through Keypoint Parameterization and Transportation Maps
Giovanni Franzese, Ravi Prakash, Cosimo Della Santina +1
Learning from Interactive Demonstrations has revolutionized the way non-expert humans teach robots. It is enough to kinesthetically move the robot around to teach pick-and-place, d…
A Unifying Variational Framework for Gaussian Process Motion Planning
Lucas Cosier, Rares Iordan, Sicelukwanda Zwane +5
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,…
Do You Need a Hand? -- a Bimanual Robotic Dressing Assistance Scheme
Jihong Zhu, Michael Gienger, Giovanni Franzese +1
Developing physically assistive robots capable of dressing assistance has the potential to significantly improve the lives of the elderly and disabled population. However, most rob…
Interactive Imitation Learning in Robotics: A Survey
Carlos Celemin, Rodrigo Pérez-Dattari, Eugenio Chisari +7
Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL) where human feedback is provided intermittently during robot execution allowing an online improvement of…
Interactive Imitation Learning of Bimanual Movement Primitives
Giovanni Franzese, Leandro de Souza Rosa, Tim Verburg +2
Performing bimanual tasks with dual robotic setups can drastically increase the impact on industrial and daily life applications. However, performing a bimanual task brings many ch…
Solving Robot Assembly Tasks by Combining Interactive Teaching and Self-Exploration
Mariano Ramirez Montero, Giovanni Franzese, Jeroen Zwanepol +1
Many high precision (dis)assembly tasks are still being performed by humans, whereas this is an ideal opportunity for automation. This paper provides a framework which enables a no…
Adaptation through prediction: multisensory active inference torque control
Cristian Meo, Giovanni Franzese, Corrado Pezzato +2
Adaptation to external and internal changes is major for robotic systems in uncertain environments. Here we present a novel multisensory active inference torque controller for indu…
Learning to Pick at Non-Zero-Velocity from Interactive Demonstrations
Anna Mészáros, Giovanni Franzese, Jens Kober
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…
ILoSA: Interactive Learning of Stiffness and Attractors
Giovanni Franzese, Anna Mészáros, Luka Peternel +1
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…