  Deployment of Reinforcement Learning (RL) algorithms for robotics
applications in the real world requires ensuring the safety of the robot and
its environment. Safe Robot RL (SRRL) is a crucial step towards achieving
human-robot coexistence. In this paper, we envision a human-centered SRRL
framework consisting of three stages: safe exploration, safety value alignment,
and safe collaboration. We examine the research gaps in these areas and propose
to leverage interactive behaviors for SRRL. Interactive behaviors enable
bi-directional information transfer between humans and robots, such as
conversational robot ChatGPT. We argue that interactive behaviors need further
attention from the SRRL community. We discuss four open challenges related to
the robustness, efficiency, transparency, and adaptability of SRRL with
interactive behaviors.
