Care-giving and assistive robotics, driven by advancements in AI, offer promising solutions to meet the growing demand for care, particularly in the context of increasing numbers of individuals requiring assistance. This creates a pressing need for efficient and safe assistive devices, particularly in light of heightened demand due to war-related injuries. While cost has been a barrier to accessibility, technological progress is able to democratize these solutions. Safety remains a paramount concern, especially given the intricate interactions between assistive robots and humans. This study explores the application of reinforcement learning (RL) and imitation learning, in improving policy design for assistive robots. The proposed approach makes the risky policies safer without additional environmental interactions. Through experimentation using simulated environments, the enhancement of the conventional RL approaches in tasks related to assistive robotics is demonstrated.

翻译：在人工智能进步的推动下，护理与辅助机器人技术为满足日益增长的照料需求（尤其是因战争相关创伤导致需辅助人数激增的背景下）提供了有前景的解决方案。这催生了对高效安全辅助设备的迫切需求，尽管成本曾是普及障碍，但技术发展正在使这些方案走向大众化。鉴于辅助机器人与人之间复杂的交互特性，安全性始终是首要关注点。本研究探索了强化学习与模仿学习在优化辅助机器人策略设计中的应用。所提出的方法无需额外环境交互即可使高风险策略变得更安全。通过仿真环境实验，证明了该方法在辅助机器人相关任务中对传统强化学习方法的改进效果。