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This paper describes a domestic service robot (DSR) that fetches everydayobjects and carries them to specified destinations according to free-formnatural language instructions. Given an instruction such as "Move the bottle onthe left side of the plate to the empty chair," the DSR is expected to identifythe bottle and the chair from multiple candidates in the environment and carrythe target object to the destination. Most of the existing multimodal languageunderstanding methods are impractical in terms of computational complexitybecause they require inferences for all combinations of target objectcandidates and destination candidates. We propose Switching Head-Tail FunnelUNITER, which solves the task by predicting the target object and thedestination individually using a single model. Our method is validated on anewly-built dataset consisting of object manipulation instructions and semiphoto-realistic images captured in a standard Embodied AI simulator. Theresults show that our method outperforms the baseline method in terms oflanguage comprehension accuracy. Furthermore, we conduct physical experimentsin which a DSR delivers standardized everyday objects in a standardizeddomestic environment as requested by instructions with referring expressions.The experimental results show that the object grasping and placing actions areachieved with success rates of more than 90%.

Switching Head-Tail Funnel UNITER for Dual Referring Expression Comprehension with Fetch-and-Carry Tasks