Natural Language as Policies: Reasoning for Coordinate-Level Embodied Control with LLMs
arxiv(2024)
摘要
We demonstrate experimental results with LLMs that address robotics task
planning problems. Recently, LLMs have been applied in robotics task planning,
particularly using a code generation approach that converts complex high-level
instructions into mid-level policy codes. In contrast, our approach acquires
text descriptions of the task and scene objects, then formulates task planning
through natural language reasoning, and outputs coordinate level control
commands, thus reducing the necessity for intermediate representation code as
policies with pre-defined APIs. Our approach is evaluated on a multi-modal
prompt simulation benchmark, demonstrating that our prompt engineering
experiments with natural language reasoning significantly enhance success rates
compared to its absence. Furthermore, our approach illustrates the potential
for natural language descriptions to transfer robotics skills from known tasks
to previously unseen tasks. The project website:
https://natural-language-as-policies.github.io/
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