対話環境における操作質問応答のための身体性指示表現
Embodied Referring Expression for Manipulation Question Answering in Interactive Environment
遠隔地へ移動して対象物を操作し質問に答える新タスクREMQAを提案し、AI2-THOR上でデータセットと3D意味再構成・モジュール型ネットワークによる手法を構築した。
著者: Qie Sima, Sinan Tan, Huaping Liu
分類: cs.RO, cs.AI
原文アブストラクト
Embodied agents are expected to perform more complicated tasks in an interactive environment, with the progress of Embodied AI in recent years. Existing embodied tasks including Embodied Referring Expression (ERE) and other QA-form tasks mainly focuses on interaction in term of linguistic instruction. Therefore, enabling the agent to manipulate objects in the environment for exploration actively has become a challenging problem for the community. To solve this problem, We introduce a new embodied task: Remote Embodied Manipulation Question Answering (REMQA) to combine ERE with manipulation tasks. In the REMQA task, the agent needs to navigate to a remote position and perform manipulation with the target object to answer the question. We build a benchmark dataset for the REMQA task in the AI2-THOR simulator. To this end, a framework with 3D semantic reconstruction and modular network paradigms is proposed. The evaluation of the proposed framework on the REMQA dataset is presented to validate its effectiveness.
