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This paper aims to address a critical challenge in robotics, which isenabling them to operate seamlessly in human environments through naturallanguage interactions. Our primary focus is to equip robots with the ability tounderstand and execute complex instructions in coherent dialogs to facilitateintricate task-solving scenarios. To explore this, we build upon the Executionfrom Dialog History (EDH) task from the Teach benchmark. We employ amulti-transformer model with BART LM. We observe that our best configurationoutperforms the baseline with a success rate score of 8.85 and agoal-conditioned success rate score of 14.02. In addition, we suggest analternative methodology for completing this task. Moreover, we introduce a newtask by expanding the EDH task and making predictions about game plans insteadof individual actions. We have evaluated multiple BART models and an LLaMA2LLM, which has achieved a ROGUE-L score of 46.77 for this task.

Interaction is all You Need? A Study of Robots Ability to Understand and Execute