SARL : Deep Reinforcement Learning based HumanAware Navigation for Mobile Robot in Indoor Environments In a humanrobot coexisting environment, reaching the goal position safely and efficiently is essential for a mobile service robot. In this paper, we present an advanced version of the Socially Attentive Reinforcement Learning (SARL) algorithm, namely SARL*, to achieve humanaware navigation in indoor environments. Recently, deep reinforcement learning has achieved great success in generating humanaware navigation policies. However, there exist some limitations in the realworld implementations: the learned navigation policies are limited to certain distances associated with the training process, and the simplification of the environment neglects obstacles other than humans. In this work, we improve the stateoftheart SARL algorithm by introducing a dynamic local goal setting mechanism and a mapbased safe action space to tackle the above problems. Realworld experimental results demonstrate that the proposed algorithm outperforms the original SARL algorithm in both time cost and path length in the humanaware navigation tasks in the indoor environment.

Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning Deep reinforcement learning has recently been widely applied in robotics to study tasks such as locomotion and grasping, but its application to social humanrobot interaction (HRI) remains a challenge. In this paper, we present a deep learning scheme that acquires a prior model of robot approaching behavior in simulation and applies it to realworld interaction with a physical robot approaching groups of humans. The scheme, which we refer to as Staged Social Behavior Learning (SSBL), considers different stages of learning in social scenarios. We learn robot approaching behaviors towards small groups in simulation and evaluate the performance of the model using objective and subjective measures in a perceptual study and a HRI user study with human participants. Results show that our model generates more socially appropriate behavior compared to a stateoftheart model.