  Building a conversational embodied agent to execute real-life tasks has been
a long-standing yet quite challenging research goal, as it requires effective
human-agent communication, multi-modal understanding, long-range sequential
decision making, etc. Traditional symbolic methods have scaling and
generalization issues, while end-to-end deep learning models suffer from data
scarcity and high task complexity, and are often hard to explain. To benefit
from both worlds, we propose JARVIS, a neuro-symbolic commonsense reasoning
framework for modular, generalizable, and interpretable conversational embodied
agents. First, it acquires symbolic representations by prompting large language
models (LLMs) for language understanding and sub-goal planning, and by
constructing semantic maps from visual observations. Then the symbolic module
reasons for sub-goal planning and action generation based on task- and
action-level common sense. Extensive experiments on the TEACh dataset validate
the efficacy and efficiency of our JARVIS framework, which achieves
state-of-the-art (SOTA) results on all three dialog-based embodied tasks,
including Execution from Dialog History (EDH), Trajectory from Dialog (TfD),
and Two-Agent Task Completion (TATC) (e.g., our method boosts the unseen
Success Rate on EDH from 6.1\% to 15.8\%). Moreover, we systematically analyze
the essential factors that affect the task performance and also demonstrate the
superiority of our method in few-shot settings. Our JARVIS model ranks first in
the Alexa Prize SimBot Public Benchmark Challenge.
