  Embodiment is an important characteristic for all intelligent agents
(creatures and robots), while existing scene description tasks mainly focus on
analyzing images passively and the semantic understanding of the scenario is
separated from the interaction between the agent and the environment. In this
work, we propose the Embodied Scene Description, which exploits the embodiment
ability of the agent to find an optimal viewpoint in its environment for scene
description tasks. A learning framework with the paradigms of imitation
learning and reinforcement learning is established to teach the intelligent
agent to generate corresponding sensorimotor activities. The proposed framework
is tested on both the AI2Thor dataset and a real world robotic platform
demonstrating the effectiveness and extendability of the developed method.
