  Human robot interaction is an exciting task, which aimed to guide robots
following instructions from human. Since huge gap lies between human natural
language and machine codes, end to end human robot interaction models is fair
challenging. Further, visual information receiving from sensors of robot is
also a hard language for robot to perceive. In this work, HuBo-VLM is proposed
to tackle perception tasks associated with human robot interaction including
object detection and visual grounding by a unified transformer based vision
language model. Extensive experiments on the Talk2Car benchmark demonstrate the
effectiveness of our approach. Code would be publicly available in
https://github.com/dzcgaara/HuBo-VLM.
