Embodied Semantic Scene Graph GenerationDownload PDF

19 Jun 2021, 10:04 (edited 01 Nov 2021)CoRL2021 PosterReaders: Everyone
  • Keywords: Semantic Scene Graph, Embodied Exploration, Learning for Visual Navigation
  • Abstract: Semantic scene graph provides an effective way for intelligent agents to better understand the environment and it has been extensively used in many robotic applications. Existing work mainly focuses on generating the scene graph from the sensory information collected from a pre-defined path, while the environment should be exhaustively explored with a carefully designed path in order to obtain a comprehensive semantic scene graph efficiently. In this paper, we propose a new task of Embodied Semantic Scene Graph Generation, which exploits the embodiment of the intelligent agent to autonomously generate an appropriate path to explore the environment for scene graph generation. To this end, a learning framework with the paradigms of imitation learning and reinforcement learning is proposed to help the agent generate proper actions to explore the environment and the scene graph is incrementally constructed. The proposed method is evaluated on the AI2Thor environment using both the quantitative and qualitative performance indexes. Additionally, we implement the proposed method on a streaming video captioning task and promising experimental results are achieved.
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