  As different research works report and daily life experiences confirm,
learning models can result in biased outcomes. The biased learned models
usually replicate historical discrimination in society and typically negatively
affect the less represented identities. Robots are equipped with these models
that allow them to operate, performing tasks more complex every day. The
learning process consists of different stages depending on human judgments.
Moreover, the resulting learned models for robot decisions rely on recorded
labeled data or demonstrations. Therefore, the robot learning process is
susceptible to bias linked to human behavior in society. This imposes a
potential danger, especially when robots operate around humans and the learning
process can reflect the social unfairness present today. Different feminist
proposals study social inequality and provide essential perspectives towards
removing bias in various fields. What is more, feminism allowed and still
allows to reconfigure numerous social dynamics and stereotypes advocating for
equality across people through their diversity. Consequently, we provide a
feminist perspective on the robot learning process in this work. We base our
discussion on intersectional feminism, community feminism, decolonial feminism,
and pedagogy perspectives, and we frame our work in a feminist robotics
approach. In this paper, we present an initial discussion to emphasize the
relevance of feminist perspectives to explore, foresee, en eventually correct
the biased robot decisions.
