most citedOn Policy Learning Robust to Irreversible Events: An Application to Robotic In-Hand Manipulation

30 citations · 38 across the 2 of their papers we have counts for

collaborators

5 papers

cs.RO2020

Incremental Skill Learning of Stable Dynamical Systems

Matteo Saveriano, Dongheui Lee

Efficient skill acquisition, representation, and on-line adaptation to different scenarios has become of fundamental importance for assistive robotic applications. In the past deca…

cs.RO2020

Merging Position and Orientation Motion Primitives

Matteo Saveriano, Felix Franzel, Dongheui Lee

In this paper, we focus on generating complex robotic trajectories by merging sequential motion primitives. A robotic trajectory is a time series of positions and orientations endi…

cs.RO2020

Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems

Matteo Saveriano, Dongheui Lee

Stable dynamical systems are a flexible tool to plan robotic motions in real-time. In the robotic literature, dynamical system motions are typically planned without considering pos…

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…

cs.RO201930 cited

On Policy Learning Robust to Irreversible Events: An Application to Robotic In-Hand Manipulation

Pietro Falco, Abdallah Attawia, Matteo Saveriano +1

In this letter, we present an approach for learning in-hand manipulation skills with a low-cost, underactuated prosthetic hand in the presence of irreversible events. Our approach…