Deployment of reinforcement learning algorithms for robotics applications in the real world requires ensuring the safety of the robot and its environment. Safe robot reinforcement learning (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.
翻译:在现实世界中,机器人应用强化学习算法的部署需要确保机器人及其环境的安全。安全机器人强化学习(SRRL)是实现人类机器人共存的关键一步。在本文中,我们设想了一个以人为本的SRRL框架,由三个阶段组成:安全探索、安全价值调整和安全合作。我们研究这些领域的研究差距,并提议利用空间机器人应用的互动行为。互动行为使得人与机器人(如对口机器人聊天GPT)之间的双向信息传输成为可能。我们主张互动行为需要空间机器人社区的进一步关注。我们讨论了与空间机器人互动行为的稳健性、效率、透明度和适应性有关的四个公开挑战。</s>