身体性を活かしたシーン記述
Towards Embodied Scene Description
エージェントが環境内を動いて最適な視点を見つけ、シーンを記述する「身体性シーン記述」を提案。模倣学習と強化学習でセンサモータ行動を学習し、AI2Thorと実機ロボットで有効性を示した。
著者: Sinan Tan, Huaping Liu, Di Guo, Xinyu Zhang, Fuchun Sun
分類: cs.RO, cs.CV
原文アブストラクト
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.
