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This paper aims to develop a framework that enables a robot to execute tasksbased on visual information, in response to natural language instructions forFetch-and-Carry with Object Grounding (FCOG) tasks. Although there have beenmany frameworks, they usually rely on manually given instruction sentences.Therefore, evaluations have only been conducted with fixed tasks. Furthermore,many multimodal language understanding models for the benchmarks only considerdiscrete actions. To address the limitations, we propose a framework for thefull automation of the generation, execution, and evaluation of FCOG tasks. Inaddition, we introduce an approach to solving the FCOG tasks by dividing theminto four distinct subtasks.

Fully Automated Task Management for Generation, Execution, and  Evaluation: A Framework for Fetch-and-Carry Tasks with Natural Language  Instructions in Continuous Space