Plan, Eliminate, and Track -- Language Models are Good Teachers for
  Embodied Agents
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.
645477c9afea4a7004a1c531
Yue Wu
645477c9afea4a7004a1c532
So Yeon Min
645477c9afea4a7004a1c533
Yonatan Bisk
admin_assigned
2023-06-02T13:25:38.204Z
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Yonatan Bisk
user
ybisk
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Ruslan Salakhutdinov
645477c9afea4a7004a1c535
Amos Azaria
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Yuanzhi Li
admin_assigned
2023-06-02T13:26:24.610Z
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Yuanzhi Li
user
Uushizhu1234
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Tom Mitchell
admin_assigned
2023-06-02T13:26:52.862Z
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Tom Mitchell
user
tommmitchell
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Shrimai Prabhumoye
admin_assigned
2023-06-02T13:27:13.134Z
6470efe7fa9fd77212e17a97
/avatars/a116b279b927627eeee9c23021b6e5ee.svg
Shrimai Prabhumoye
user
shrimai
Plan, Eliminate, and Track -- Language Models are Good Teachers for
  Embodied Agents
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.
The PET framework leverages pre-trained large language models to simplify control tasks in non-text environments, improving generalization on instruction following benchmarks.
pre-trained large language models LLMs transformer architecture action scoring action modeling Plan module Eliminate module Track module AlfWorld instruction following benchmark