A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors
This work tackles safety issues in robot reinforcement learning for human-robot coexistence, but it is incremental as it builds on existing concepts without presenting new experimental results.
The paper addresses the problem of ensuring safety in robot reinforcement learning for real-world deployment by proposing a human-centered framework with three stages and highlighting the role of interactive behaviors. It identifies research gaps and discusses four open challenges related to robustness, efficiency, transparency, and adaptability.
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.