We demonstrate experimental results with LLMs that address robotics action
planning problems. Recently, LLMs have been applied in robotics action
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
action planning through natural language reasoning, and outputs coordinate
level control commands, thus reducing the necessity for intermediate
representation code as policies. 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.

我们展示了使用 LLMs 解决机器人动作规划问题的实验结果。与其它方法不同，我们的方法通过自然语言推理获取任务和场景对象的文本描述，并输出坐标级控制命令，从而减少中间表示代码作为策略的必要性。我们的方法在多模态提示仿真基准上进行评估，证明了自然语言推理改善成功率的潜力，并展示了利用自然语言描述将机器人技能从已知任务转移到以前未见任务的可能性。