Large Language Models as Generalizable Policies for Embodied Tasks
Authors: name: Andrew Szot, name: Max Schwarzer, name: Harsh Agrawal, name: Bogdan Mazoure, name: Walter Talbott, name: Katherine Metcalf, name: Natalie Mackraz, name: Devon Hjelm, name: Alexander Toshev

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.