Editorial Variable Impedance Control and Learning in Complex Interaction Scenarios: Challenges and Opportunities

Fares J. Abu-Dakka*, Matteo Saveriano, Meghan E. Huber, Thiago Boaventura

*Corresponding author for this work

Research output: Contribution to journalEditorialScientific

Abstract

The papers in this special section focus on variable impedance control and learning in complex interaction applications. Increasingly, robots are expected to enter various application scenarios and interact with unknown and dynamically changing environments. More specifically, we are expecting robots to be out from their caged industrial workspace and operate among us in our dynamic and uncertain environments. To have robots safely and robustly interacting with their surroundings and cooperating with people, they must present some degree of mechanical compliance. While such compliance can be achieved passively, using elastic and flexible elements in the robot’s mechanical design, control algorithms are able to shape the robot’s mechanical compliance (e.g., stiffness, damping, and inertia) in a much more versatile manner. A well-known approach to control physical interaction is via impedance control, where it is possible to control the robot’s mechanical impedance (i.e., the dynamic relation between force and velocity) at a given interaction point.
Original languageEnglish
Pages (from-to)12158-12160
Number of pages3
JournalIEEE Robotics and Automation Letters
Volume7
Issue number4
DOIs
Publication statusPublished - 1 Oct 2022
MoE publication typeB1 Non-refereed journal articles

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