Purpose of Review: To discuss the current state of reproducibility of research in human-robot interaction (HRI), challenges specific to the field, and recommendations for how the community can support reproducibility. Recent Findings: As in related fields such as artificial intelligence, robotics, and psychology, improving research reproducibility is key to the maturation of the body of scientific knowledge in the field of HRI. The ACM/IEEE International Conference on Human-Robot Interaction introduced a theme on Reproducibility of HRI to their technical program in 2020 to solicit papers presenting reproductions of prior research or artifacts supporting research reproducibility. Summary: This review provides an introduction to the topic of research reproducibility for HRI and describes the state of the art in relation to the HRI 2020 Reproducibility theme. As a highly interdisciplinary field that involves work with technological artifacts, there are unique challenges to reproducibility in HRI. Biases in research evaluation and practice contribute to challenges in supporting reproducibility, and the training of researchers could be changed to encourage research reproduction. The authors propose a number of solutions for addressing these challenges that can serve as guidelines for the HRI community and related fields.

Robots intended for social contexts are often designed with explicit humanlike attributes in order to facilitate their reception by (and communication with) people. However, observation of an "uncanny valley" - a phenomenon in which highly humanlike entities provoke aversion in human observers - has lead some to caution against this practice. Both of these contrasting perspectives on the anthropomorphic design of social robots find some support in empirical investigations to-date. Yet, owing to outstanding empirical limitations and theoretical disputes, the uncanny valley and its implications for human-robot interaction remains poorly understood. We thus explored the relationship between human similarity and people's aversion towards humanlike robots via manipulation of the agents' appearances. Here we employed a picture-viewing task (N_{agents}=60) to conduct an experimental test (N_{participants}=72) of the uncanny valley's existence and the visual features that cause certain humanlike robots to be unnerving. Across the levels of human similarity, we further manipulated agent appearance on two dimensions, typicality (prototypic, atypical, and ambiguous) and humanness (robot, human), and measured participants' aversion using both subjective and behavioral indices. Our findings were as follows: (1) Further substantiating its existence, the data show a clear and consistent uncanny valley in the current design space of humanoid robots. (2) Both category ambiguity, and more so, atypicalities provoke aversive responding, thus shedding light on the visual factors that drive people's discomfort. (3) Use of the Negative Attitudes towards Robots Scale did not reveal any significant relationships between people's pre-existing attitudes towards humanlike robots and their aversive responding -- suggesting positive exposure and/or additional experience with robots is unlikely to affect the occurrence of an uncanny valley effect in humanoid robotics. This work furthers our understanding of both the uncanny valley, as well as the visual factors that contribute to an agent's uncanniness.