Large Language Models as Generalizable Policies for Embodied Tasks
We show that large language models (LLMs) can be adapted to be generalizable
policies for embodied visual tasks. Our approach, called Large LAnguage model
Reinforcement Learning Policy (LLaRP), adapts a pre-trained frozen LLM to take
as input text instructions and visual egocentric observations and output
actions directly in the environment. Using reinforcement learning, we train
LLaRP to see and act solely through environmental interactions. We show that
LLaRP is robust to complex paraphrasings of task instructions and can
generalize to new tasks that require novel optimal behavior. In particular, on
1,000 unseen tasks it achieves 42% success rate, 1.7x the success rate of other
common learned baselines or zero-shot applications of LLMs. Finally, to aid the
community in studying language conditioned, massively multi-task, embodied AI
problems we release a novel benchmark, Language Rearrangement, consisting of
150,000 training and 1,000 testing tasks for language-conditioned
rearrangement. Video examples of LLaRP in unseen Language Rearrangement
instructions are at https://llm-rl.github.io.
653f1125059fdd2587090c4f
Andrew Szot
claimed_verified
2023-10-30T15:08:49.745Z
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Andrew
user
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Max Schwarzer
admin_assigned
2023-10-30T14:15:39.279Z
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Max Schwarzer
user
MaxSchwarzer
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Harsh Agrawal
653f1125059fdd2587090c52
Bogdan Mazoure
admin_assigned
2023-10-30T14:16:18.919Z
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Bogdan Mazoure
user
bmazoure
653f1125059fdd2587090c53
Walter Talbott
653f1125059fdd2587090c54
Katherine Metcalf
claimed_verified
2024-07-19T11:25:27.821Z
669968174bbe8ad52e889152
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Rin Metcalf Susa
user
kmetcalf
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Natalie Mackraz
653f1125059fdd2587090c56
Devon Hjelm
653f1125059fdd2587090c57
Alexander Toshev
Large Language Models as Generalizable Policies for Embodied Tasks
We show that large language models (LLMs) can be adapted to be generalizable
policies for embodied visual tasks. Our approach, called Large LAnguage model
Reinforcement Learning Policy (LLaRP), adapts a pre-trained frozen LLM to take
as input text instructions and visual egocentric observations and output
actions directly in the environment. Using reinforcement learning, we train
LLaRP to see and act solely through environmental interactions. We show that
LLaRP is robust to complex paraphrasings of task instructions and can
generalize to new tasks that require novel optimal behavior. In particular, on
1,000 unseen tasks it achieves 42% success rate, 1.7x the success rate of other
common learned baselines or zero-shot applications of LLMs. Finally, to aid the
community in studying language conditioned, massively multi-task, embodied AI
problems we release a novel benchmark, Language Rearrangement, consisting of
150,000 training and 1,000 testing tasks for language-conditioned
rearrangement. Video examples of LLaRP in unseen Language Rearrangement
instructions are at https://llm-rl.github.io.
LLaRP, a reinforcement learning approach adapting pre-trained large language models for embodied visual tasks, achieves high success rates on unseen tasks and generalizes robustly to new instructions.
Large Language models LLaRP reinforcement learning embodied visual tasks text instructions visual egocentric observations generalization paraphrasings Language Rearrangement benchmark language-conditioned rearrangement tasks