Natural Language As Policies: Reasoning For Coordinate-level Embodied Control With Llms

Mikami Yusuke, Melnik Andrew, Miura Jun, Hautamäki Ville. Arxiv 2024

[Paper] [Code]    
Applications Has Code Prompting Reinforcement Learning Tools

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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