Recent years have seen an increasing amount of work on embodied AI agents
that can perform tasks by following human language instructions. However, most
of these agents are reactive, meaning that they simply learn and imitate
behaviors encountered in the training data. These reactive agents are
insufficient for long-horizon complex tasks. To address this limitation, we
propose a neuro-symbolic deliberative agent that, while following language
instructions, proactively applies reasoning and planning based on its neural
and symbolic representations acquired from past experience (e.g., natural
language and egocentric vision). We show that our deliberative agent achieves
greater than 70% improvement over reactive baselines on the challenging TEACh
benchmark. Moreover, the underlying reasoning and planning processes, together
with our modular framework, offer impressive transparency and explainability to
the behaviors of the agent. This enables an in-depth understanding of the
agent's capabilities, which shed light on challenges and opportunities for
future embodied agents for instruction following. The code is available at
this https URL.

本文提出了一种基于神经和符号表示的推理代理，它在遵循语言指令的同时，基于过去的经验（例如自然语言和自我中心视觉）积极应用推理和计划，从而实现了大于 70％ 的改进，并提供了令人印象深刻的透明度和可解释性。