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hello there today were looking at language models as zero shot planners extracting actionable knowledge for embodied agents and im going to interview the first author wen long huang in a few minutes so first theres an explanation of the paper 10 15 minutes or so im gonna try to keep to it and then we jump into the interview where we can discuss this paper at length on a high level this paper asks can we use the knowledge that is inherent in large language models like gpt3 or surprisingly open ais codecs in order to do planning in what they call embodied agents ultimately its going to be this environment right here the uh i dont even know what its the virtual home environment and its about a virtual home you have to fulfill some tasks like brushed your teeth then the model has to come up with a sequence of steps that are admissible by the environment so theres a level of admissibility of action predefined actions that are admissible the model has to come up with these actions
