activity
20192021
most citedA Transfer Learning Approach to Cross-Modal Object Recognition: From Visual Observation to Robotic Haptic Exploration

42 citations · 80 across the 4 of their papers we have counts for

collaborators

8 papers

cs.RO2021

Learning Deep Energy Shaping Policies for Stability-Guaranteed Manipulation

Shahbaz Abdul Khader, Hang Yin, Pietro Falco +1

Deep reinforcement learning (DRL) has been successfully used to solve various robotic manipulation tasks. However, most of the existing works do not address the issue of control st…

cs.RO2020

Learning Behavior Trees with Genetic Programming in Unpredictable Environments

Matteo Iovino, Jonathan Styrud, Pietro Falco +1

Modern industrial applications require robots to be able to operate in unpredictable environments, and programs to be created with a minimal effort, as there may be frequent change…

cs.RO2020

Learning Stable Normalizing-Flow Control for Robotic Manipulation

Shahbaz Abdul Khader, Hang Yin, Pietro Falco +1

Reinforcement Learning (RL) of robotic manipulation skills, despite its impressive successes, stands to benefit from incorporating domain knowledge from control theory. One of the…

cs.RO2020

Stability-Guaranteed Reinforcement Learning for Contact-rich Manipulation

Shahbaz A. Khader, Hang Yin, Pietro Falco +1

Reinforcement learning (RL) has had its fair share of success in contact-rich manipulation tasks but it still lags behind in benefiting from advances in robot control theory such a…

cs.RO202042 cited

A Transfer Learning Approach to Cross-Modal Object Recognition: From Visual Observation to Robotic Haptic Exploration

Pietro Falco, Shuang Lu, Ciro Natale +2

In this work, we introduce the problem of cross-modal visuo-tactile object recognition with robotic active exploration. With this term, we mean that the robot observes a set of obj…

cs.RO20198 cited

A Human Action Descriptor Based on Motion Coordination

Pietro Falco, Matteo Saveriano, Eka Gibran Hasany +2

In this paper, we present a descriptor for human whole-body actions based on motion coordination. We exploit the principle, well known in neuromechanics, that humans move their joi…