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.

TendencyRL: Multistage Discriminative Hints for Efficient GoalOriented Reverse Curriculum Learning Deep reinforcement learning algorithms have been proven successful in a variety of simulation tasks with dense reward feedback. However, realworld RL applications, e.g. robotic manipulation, remain challenging as most of them are multistage and a positive reward can only be received when the final goal is accomplished. In this work, we propose a potential solution to such problems with the introduction of an experiencebased tendency reward shaping mechanism, which provides the robot with additional hints based on a discriminative learning on past experience. The reward along with a stageawareness network help accelerate solving a multistage task split into shorter phases in a reverse curriculum learning manner. We extensively study the advantages of TRL on the standard longterm goaloriented robotics domains such as pickandplace, and show that TRL performs more efficiently and robustly than prior approaches in tasks with large state space. In addition, we demonstrate that TRL can solve difficult robot manipulation challenges directly from perception.