What is recorded here?
“Is there an apple in the refrigerator?” cannot always be answered from the first camera image. The agent may need to move, open the door and remember what it saw. Gordon and colleagues introduce an interactive question-answering task and a hierarchical system that divides planning, navigation, detection, manipulation and answering. A spatial memory stores observations at locations rather than forcing a single recurrent state to remember everything. Their IQADATA resource supplies automatically generated existence, counting and spatial-relation questions in simulated kitchens. The contribution combines this task design with the specific memory and controller architecture, without separately counting the underlying AI2-THOR engine.
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Scientific scope
The original split uses 25 training kitchens and five test kitchens; questions follow templates rather than unrestricted human conversation. Training uses simulator-provided supervision, and some reported results additionally use ideal detections or navigation. Those results must be distinguished from learned-perception results. The learned system does not outperform simple answer-frequency guessing on every question type. No real-house robot experiment or general language-understanding guarantee is established.
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People and systems
Daniel Gordon; Aniruddha Kembhavi; Mohammad Rastegari; Joseph Redmon; Dieter Fox; Ali Farhadi
Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred.
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English catalogue narrative
IQA: asking questions that require an agent to look and act
“Is there an apple in the refrigerator?” cannot always be answered from the first camera image. The agent may need to move, open the door and remember what it saw. Gordon and colleagues introduce an interactive question-answering task and a hierarchical system that divides planning, navigation, detection, manipulation and answering. A spatial memory stores observations at locations rather than forcing a single recurrent state to remember everything. Their IQADATA resource supplies automatically generated existence, counting and spatial-relation questions in simulated kitchens. The contribution combines this task design with the specific memory and controller architecture, without separately counting the underlying AI2-THOR engine.
Scientific scope
The original split uses 25 training kitchens and five test kitchens; questions follow templates rather than unrestricted human conversation. Training uses simulator-provided supervision, and some reported results additionally use ideal detections or navigation. Those results must be distinguished from learned-perception results. The learned system does not outperform simple answer-frequency guessing on every question type. No real-house robot experiment or general language-understanding guarantee is established.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Daniel Gordon; Aniruddha Kembhavi; Mohammad Rastegari; Joseph Redmon; Dieter Fox; Ali Farhadi
Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred.