Abstract Robotics in healthcare has recently emerged, backed by the recent advances in the fifield of machine learning and robotics. Researchers are focusing on training robots for interacting with elderly adults. This research primarily focuses on engineering more effificient robots that can learn from their mistakes, thereby aiding in better humanrobot interaction. In this work, we propose a method in which a robot learns to navigate itself to the individual in need. The robotic agents’ learning algorithm will be capable of navigating in an unknown environment. The robot’s primary objective is to locate human in a house, and upon fifinding the human, the goal is to interact with them while complementing their pose and gaze. We propose an end to end learning strategy, which uses a recurrent neural network architecture in combination with Qlearning to train an optimal policy. The idea can be a contribution to better human-robot interaction