  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.
