Plan, Eliminate, and Track -- Language Models are Good Teachers for
  Embodied Agents
Authors: name: Yue Wu, name: So Yeon Min, name: Yonatan Bisk, name: Ruslan Salakhutdinov, name: Amos Azaria, name: Yuanzhi Li, name: Tom Mitchell, name: Shrimai Prabhumoye

Pre-trained large language models (LLMs) capture procedural knowledge about
the world. Recent work has leveraged LLM's ability to generate abstract plans
to simplify challenging control tasks, either by action scoring, or action
modeling (fine-tuning). However, the transformer architecture inherits several
constraints that make it difficult for the LLM to directly serve as the agent:
e.g. limited input lengths, fine-tuning inefficiency, bias from pre-training,
and incompatibility with non-text environments. To maintain compatibility with
a low-level trainable actor, we propose to instead use the knowledge in LLMs to
simplify the control problem, rather than solving it. We propose the Plan,
Eliminate, and Track (PET) framework. The Plan module translates a task
description into a list of high-level sub-tasks. The Eliminate module masks out
irrelevant objects and receptacles from the observation for the current
sub-task. Finally, the Track module determines whether the agent has
accomplished each sub-task. On the AlfWorld instruction following benchmark,
the PET framework leads to a significant 15% improvement over SOTA for
generalization to human goal specifications.