Robot Learning
This section covers robot learning within robotics and automation engineering. It welcomes theory, methods, system design and applications when the technical contribution is explicit and supported by analysis, simulation or testing suited to the claim. Authors should report platforms, tasks, evaluation conditions, comparison methods and limitations clearly.
Published by EJSIR Publisher. This section is part of the journal’s defined subject scope and is used to guide author selection and editorial routing.
Section scope
This section covers robot learning within robotics and automation engineering. It welcomes theory, methods, system design and applications when the technical contribution is explicit and supported by analysis, simulation or testing suited to the claim. Authors should report platforms, tasks, evaluation conditions, comparison methods and limitations clearly.
Example topics
The following examples illustrate the section’s subject range; they are not an exhaustive list.
- Models, control strategies, system architectures or design methods for robot learning.
- Simulation, laboratory or field evaluations of robotics and automation methods in robot learning, with documented tasks and performance measures.
- Comparative or applied engineering studies connecting robot learning with safety, reliability, human factors or system integration.
Scope boundary
Submissions that mention robot learning but do not make a substantive contribution, use methods appropriate to the question or support conclusions with suitable evidence fall outside this section’s scope.
Editorial assessment
Once the journal opens, a qualified editor will assess section fit and, where appropriate, coordinate independent peer review. Editors will make decisions under the journal’s published review and ethics policies.