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Pietro Pustina

dblp:316/9708 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2024
0000-0003-0368-5626ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Motion planning and robot control · 87% Robot manipulation · 13%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control › learning control
iterative learning control
0.712023
Experimental Validation of Functional Iterative Learning Control on a One-Link Flexible Arm · ICRA 2023
Robotics › Motion planning and robot control
robot control
0.712023
Experimental Validation of Functional Iterative Learning Control on a One-Link Flexible Arm · ICRA 2023
Robotics › Robot manipulation › soft robotics
soft robot control
0.212024
Input Decoupling of Lagrangian Systems via Coordinate Transformation: General Characterization and Its Application to Soft Robotics · IEEE Trans. Robotics 2024
Robotics › Motion planning and robot control › robot control
flexible manipulator control
0.212023
Experimental Validation of Functional Iterative Learning Control on a One-Link Flexible Arm · ICRA 2023

Methods — techniques the papers use, named apart from their topics

power invariance · 0.8euler-lagrange dynamics · 0.8coordinate transformation · 0.8stepper motor · 0.7functional iterative learning control · 0.7
YearPublicationVenuePosition
2024 Input Decoupling of Lagrangian Systems via Coordinate Transformation: General Characterization and Its Application to Soft Robotics
abstract
Suitable representations of dynamical systems can simplify their analysis and control. On this line of thought, this paper aims to answer the following question:Can a transformation of the generalized coordinates under which the actuators directly perform work on a subset of the configuration variables be found?Not only we show that the answer to this question isyes, but we also provide necessary and sufficient conditions. More specifically, we look for a representation of the configuration space such that the right-hand side of the dynamics in Euler-Lagrange form becomes [IO]tu, being u the system input. We identify a class of systems, calledcollocated, for which this problem is solvable. Under mild conditions on the input matrix, a simple test is presented to verify whether a system is collocated or not. By exploiting power invariance, we provide necessary and sufficient conditions that a change of coordinates decouples the input channels if and only if the dynamics is collocated. In addition, we use the collocated form to derive novel controllers for damped underactuated mechanical systems. To demonstrate the theoretical findings, we consider several Lagrangian systems with a focus on continuum soft robots.
Pietro Pustina, Cosimo Della Santina, Frédéric Boyer, Alessandro De Luca 0001, Federico Renda
IEEE Trans. Robotics1
2023 Experimental Validation of Functional Iterative Learning Control on a One-Link Flexible Arm
abstract
Performing precise, repetitive motions is essential in many robotic and automation systems. Iterative learning control (ILC) allows determining the necessary control command by using a very rough system model to speed up the process. Functional iterative learning control is a novel technique that promises to solve several limitations of classic ILC. It operates by merging the input space into a large functional space, resulting in an over-determined control task in the iteration domain. In this way, it can deal with systems having more outputs than inputs and accelerate the learning process without resorting to model discretizations. However, the framework lacks so far a validation in experiments. This paper aims to provide such experimental validation in the context of robotics. To this end, we designed and built a one-link flexible arm that is actuated by a stepper motor, which makes the development of an accurate model more challenging and the validation closer to the industrial practice. We provide multiple experimental results across several conditions, proving the feasibility of the method in practice.
Sjoerd Drost, Pietro Pustina, Franco Angelini, Alessandro De Luca 0001, Gerwin Smit, Cosimo Della Santina
ICRA2