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Embodied agents are expected to perform more complicated tasks in aninteractive environment, with the progress of Embodied AI in recent years.Existing embodied tasks including Embodied Referring Expression (ERE) and otherQA-form tasks mainly focuses on interaction in term of linguistic instruction.Therefore, enabling the agent to manipulate objects in the environment forexploration actively has become a challenging problem for the community. Tosolve this problem, We introduce a new embodied task: Remote EmbodiedManipulation Question Answering (REMQA) to combine ERE with manipulation tasks.In the REMQA task, the agent needs to navigate to a remote position and performmanipulation with the target object to answer the question. We build abenchmark dataset for the REMQA task in the AI2-THOR simulator. To this end, aframework with 3D semantic reconstruction and modular network paradigms isproposed. The evaluation of the proposed framework on the REMQA dataset ispresented to validate its effectiveness.

Embodied Referring Expression for Manipulation Question Answering in Interactive Environment