Following ahead companion robot

Nowadays, most intelligent systems rely on interacting with humans. Two main functionalities of such systems are the ability to follow their users and to predict their future motions. This thesis develops robust methods for a companion robot that can follow humans and predict their motions in the future. Predicting plausible human motion is one of the most critical and challenging parts of human-robot interaction (HRI) applications. We can categorize human motion prediction into probabilistic or deterministic approaches. The probabilistic approach tries to model the multi-modality of human motion; in contrast, the deterministic approach has one output per observation. In this thesis, we design two human motion prediction methods. One of them utilizes the multimodality of human motion for accurate predictions, while the other one is deterministic and fast. Additionally, we design two human-following methods one based on reinforcement learning and the other using a human motion prediction model. The first work investigates a hybrid solution that combines deep reinforcement learning (RL) and classical trajectory planning for the following in-front application. As for the second method, we design a general human-following system with a fast non-autoregressive human motion prediction model.